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Full text of "Articulator involvement in naming : a test of the articulatory feedback hypothesis of naming"

ARTICULATOR INVOLVEMENT IN NAMING- 
A TEST OF THE ARTICULATORY FEEDBACK HYPOTHESIS OF NAMING 



By 

LISA HSIAO-JUNG LU 



A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL 

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT 

OF THE REQUIREMENTS FOR THE DEGREE OF 

DOCTOR OF PHILOSOPHY 

UNIVERSITY OF FLORIDA 

2000 



' ACKNOWLEDGMENT 

I would like to thank each of my committee members for their guidance: Eileen 
Fennell for her clinical wisdom, conceptual inquiry, and generous support; Ken Heilman 
for the opportunity to approach neuropsychology through clinical hypothesis testing; 
Bruce Crosson for his belief in my abilities; Duane Dede for helping me think broadly; 
and Jamie Algina for his patient and thorough teaching of statistics. I have been very 
lucky in crossing the paths of mentors who have such vigor and integrity both 
professionally and personally. I would also like to thank Ann Alexander, Linda 
Lombardino, Tim Conway, and the American Psychological Association for their support 
of this project. To my family, especially to my husband, I would like to say thank you 
for your undying support through these most challenging years. 






TABLE OF CONTENTS 

Page 
ACKNOWLEDGMENT 

LIST OF TABLES 

VI 

LIST OF FIGURES . 

IX 

ABSTRACT .. 

X 

INTRODUCTION 

Development of the Hypothesis I 

Liberman's Motor Theory of Speech Perception ' ] 

Heilman's Motor- Articulatory Feedback Hypothesis ' ... . 3 

Anatomy of the Articulatory Feedback System 4 

Proposed Hypothesis: Articulatory Feedback Hypothesis of Naming 6 

Developmental Phonological Dyslexia g 

Definition ofDevelopmental Phonological Dyslexia g 

Nature and Extent of Naming Deficit .....'' 14 

Role of Phonological Awareness jg 

Anatomical Evidence of Anomalies 20 

Co-morbidity with Attention-Deficit/Hyperactivity Disorder 26 

Research Questions 27 

What Is the Correlation Between Articulatory Knowledge and 

Naming? 28 

Do Dyslexics Have Worse Articulatory Knowledge? 28 

Is There Support for the Articulatory Feedback Hypothesis of 

Naming? ^p 

What Is the Relationship Between Articulatory Knowledge and 

Phonological Awareness? 3q 

METHODS 

32 

Subjects 

Descriptive Measures " -^ 

Articulatory Awareness Test 30 

Naming ••••■■•.^''^^.^^.^^.^!^!!^^^^'!.' 40 

iii 



Phonological Awareness 4j 

Attention-Deficit/Hyperactivity Disorder 41 

Experimental Measures 42 

Naming Assessed via Phoneme Match (NAPM) ' . 42 

Visual Match 45 

Phoneme Match 4^ 

Naming Test 4-7 

Procedures 40 

RESULTS 5j 

Articulatory Knowledge - 51 

Phonological ly Impaired vs. Controls 53 

Articulatory Awareness Test 54 

Descriptive Measures 55 

Experimental Measures 57 

Predictorsof Articulatory Knowledge 68 

Predictors of Phonological Awareness 70 

Developmental Phonological Dyslexics vs. Adequate Readers with 

Poor Phonology vs. Controls 72 

Articulatory Awareness Test 73 

Descriptive Measures 75 

Experimental Measures 76 

Predictorsof Articulatory Knowledge ..^..'. g] 

Predictors of Phonological Awareness 84 

Poor vs. Adequate Articulatory Knowledge 85 

Descriptive Measures g^ 

Expenmental Measures gg 

DISCUSSION 

Review of Hypothesis oy 

Correlation Between Articulatory Knowledge and Name Retrieval 1 00 

Group Differences in Articulatory Knowledge IO3 

Group Differences on Naming Measures 104 

Reaction Time and Response Accuracy 106 

Interference Movement Frequency HO 

Attention-Deficit/Hyperactivity Disorder 1 1 3 

Relationship Between Articulatory Knowledge and Phonological 

Awareness .,. 

Articulatory Feedback Hypothesis of Naming 110 

Limitations '* 

Summary of Findings ,^-. 

Correlation Between Articulatory Knowledge and Naming 123 

Dyslexics Do Not Have Worse Articulatory Knowledge 123 

Relationship Between Articulatory Knowledge and Phonological 

■s 

iv 



Awareness j24 

Modification oftheArticulatory Feedback Hypothesis of Naming .... 124 

APPENDIX 1 ARTICULATORY AWARENESS TEST 125 

APPENDIX 2 ATTENTION-DEFICIT/HYPERACTIVITY DISORDER 

INTERVIEW J35 

APPENDIX 3 NAPM STIMULI 139 

REFERENCES j4j 

BIOGRAPHICAL SKETCH 14^ 



, LIST OF TABLES 

Table „ 
Page 

L Summary of grouping criteria 35 

2. Summary ofdemographics and grouping criteria scores 38 

3. AAT scores of the Morris Center population 40 

4. Sample of the chart for determining the order of task, interference, 

and stimulus set for each subject ' 49 

5. Order of test administration 5q 

6. Pearson correlations between the Articulatory Awareness Test (AAT) 

score and reaction time on experimental measures ^ . . . 53 

7. AAT and AAT-R scores obtained by Phonologically Impaired (PI) 

and Control (CTRL) groups 54 

8. Pearson correlations between the AAT score and reaction time on 
experimental measures for PI and CTRL groups 55 

9. PI and CTRL groups' performance on descriptive measures 56 

10. Means and standard deviations of reaction time (RT in milliseconds) 

and accuracy (% Correct) on the Phoneme Match Test 53 

1 1 . Reaction time and accuracy on the NAPM and Visual Match Tests 

for PI and CTRL groups ^g 

12. Means and standard deviations ofreaction time for each block 61 

1 3 Response accuracy (percentage) reflecting the 

Task X Interference X Block interaction 52 

14. Comparison of overall findings with Block 1 and Block 2 findings 63 

vi 



15. Block 1 reaction time and accuracy on the NAPM and Visual 

Match Tests for PI and CTRL groups 64 

16. Block 2 reaction time and accuracy on the NAPM and Visual 

Match Tests for PI and CTRL groups 64 

17. Reaction time and accuracy of the Non-ADHD and ADHD subgroups 

on the NAPM and Visual Match Tests 66 

18. Interfering movement frequency index (i.e., number of movements 

per second) for the PI and CTRL groups 68 

19. Pearson correlations beUveen the AAT score and interfering movement 
index for PI and CTRL groups 69 

20. Pearson correlations between the AAT score and variables entered 

into stepwise regression analysis for PI and CTRL groups 70 

2 1 . Pearson correlations between the LAC score and variables entered 

into stepwise regression analysis for PI and CTRL groups 71 

22. Means and standard deviations of AAT scores obtained by DPD 

ARPP, and CTRL groups _ ' 73 

23. Pearson correlations between the AAT score and reaction time on 
experimental measures for DPD, ARPP, and CTRL groups 74 

24. Means and standard deviations on descriptive measures for the 

DPD, ARPP, and CTRL groups 75 

25. Reaction time and accuracy on the Phoneme Match Test for 

DPD, ARPP, and CTRL groups 76 

26. Reaction time and accuracy on the NAPM and Visual Match Tests 

for DPD, ARPP, and CTRL groups 77 

27. Reaction time and accuracy of the phonologically impaired 

Non-ADHD and ADHD subgroups 79 

28. Interfering movement frequency index for the DPD ARPP and 

CTRL groups ' ' ^^ 

29. Pearson correlations between the AAT score and interfenng movement 
frequency mdex for DPD, ARPP, and CTRL groups 82 



VII 



■.'■^■-- 



30. Pearson correlations between the AAT score and variables entered 

into stepwise regression analysis for DPD and ARPP groups 83 

31. Pearson correlations between the LAC score and variables entered 

into stepwise regression analysis for DPD and ARPP groups 85 

32. Demographics of the Poor Articulatory Knowledge (P AK) and 

Adequate Articulatory Knowledge (AAK) groups 86 

33. Means and standard deviations on descriptive measures for the 

PAKand AAK groups gy 

34. Reaction time and accuracy on the Phoneme Match Test for the 

PAKand AAK groups gg 

35. Reaction time and accuracy on the NAPM for PAK and AAK groups. 
Numbers represent data without the covariate extracted 89 

36. Reaction time and accuracy on the Visual Match Test for PAK 

and AAK groups 93 

37. Interfering movement frequency index for the PAK and AAK groups 95 



vni 



LIST OF FIGURES 



Figure 



Page 



1. A simplified model ofreadingfrom Ellis and Young (1988) U 

2. Block 1 NAPM reaction time, plotted against the ability to match end 
phonemes ni 

3. Block 2 NAPM reaction time, plotted against the ability to match end 
phonemes o^j 

4. Formula for calculating the effect size reflecting the Group X Task X 
Interference interaction 9^ 



IX 



Abstract of Dissertation Presented to the Graduate School 

of the University of Florida in Partial Fulfillment of the 
Requirements for the Degree of Doctor of Philosophy 

ARTICULATOR INVOLVEMENT IN NAMING- 
A TEST OF THE ARTICULATORY FEEDBACK HYPOTHESIS OF NAMING 

By 

LISA HSIAO-JUNG LU 
August, 2000 

Chair: Eileen B. Fennell 

Major Department: Clinical and Health Psychology 

The articulator/ feedback hypothesis of naming posited that articulatory feedback 
facilitates name retrieval. This was tested using an interference paradigm. Naming 
performance dunng a condition that allowed for articulatory feedback was contrasted 
with a condition that interfered with articulatory feedback by providing inappropriate 
articulatory feedback. Because Montgomery found that dyslexic children had impaired 
articulatory knowledge, performance of phonologically impaired readers was contrasted 
with those of normal readers. Subjects were also grouped by their level of articulatory 
knowledge, and performance between knowledgeable and unknowledgeable groups was 
compared. One assumption of the hypothesis was that those with adequate articulatory 
knowledge would benefit from articulator feedback while those with poor articulatory 
knowledge would not. The hypothesis predicted that interfering with articulatory 
feedback would affect subjects who have articulatory knowledge by removing the 



facilitation effects provided by articulatory feedback. Results did not directly support the 
hypothesis. For individuals with articulatory knowledge, naming latency during the 
condition that allowed for articulatory feedback was not better than the condition that 
interfered with feedback. Subjects did not spontaneously use articulatory feedback to 
assist name retrieval. However, other data did suggest a relationship between articulatory 
knowledge and name retrieval. Among individuals with poor articulatory knowledge, 
mappropriate articulatory feedback and name retrieval interfered with each other and 
competed for neural resources. This suggested a neural connectivity between articulatory 
knowledge and name retrieval that was not evident between articulatory knowledge and a 
nonverbal control task. Those with articulatory knowledge appeared to have processed 
name retrieval automatically and efficiently, and they had sufficient extra neural 
resources to process extraneous information such as interfering feedback. In contrast, 
those with poor articulatory knowledge retrieved names less efficiently. They had 
limited capacity to simultaneously process interfering information while engaging in 
name retrieval. It was also found that articulatory knowledge and phonological 
awareness were dissociable phenomena. Both normal and phonologically impaired 
readers demonstrated a wide range of articulatory knowledge, and dyslexic children did 
not have worse articulatory knowledge. 



XI 



INTRODUCTION .,, 

The problem of name retrieval is one that has received extensive attention in the 
neuropsychology literature. By focusing on this limited aspect of language, researchers 
hope to generalize the knowledge learned here to other aspects of linguistic functioning. 
The term name retneval has been defined differently by different research groups. Here, 
name retrieval refers only to the activation of phonological representation of a word and 
does not include activation of motor patterns to produce such a representation. The 
current project proposes to test a new hypothesis of name retrieval, the articulatory 
feedback hypothesis of naming. First, the development of this hypothesis from 
Liberman's motor theory of speech perception and Heilman's theory of motor-articulatory 
feedback will be presented. Then the articulatory feedback hypothesis of naming will be 
proposed. This hypothesis was tested with a population of children who have 
phonological dyslexia of the developmental type. Therefore a discussion of dyslexia, 
related name retrieval issues, neurological anatomy of this population, and co-morbidity 
with Attention-Deficit/Hyperactivity Disorder (ADHD) will follow. 

Development of the Hvp othesis 
Liberman's Motor Theory of Speech Perception 

Liberman, Cooper, Shankweiler, and Studdert-Kennedy first proposed their motor 
theoiy of speech perception in 1967. Extensive research following the first proposal of 

1 



2 
their theory has led to subsequent revisions of their theory, the most recent of which was 

presented in Liberman and Mattingly, 1985. Their current theory on speech perception 
was based on two tenets: 1) The object of speech perception is the intended gestures of 
the speaker; 2) speech perception and speech production are innately (i.e., biologically) 
linked, not learned. 

The first tenet of their theory speaks to why this theory is a "motor" one instead of 
a "sensory" one. Unlike auditory theories, which posit that perception of speech depends 
on an analysis of auditory signals, Liberman and Mattingly ( 1 985) proposed that the goal 
of speech perception is not to uncode the auditory signals, but to infer the intended 
gestures of the speaker's vocal system. They argued that the uncoding of auditory cues 
cannot be sufficient for speech perception because there is no correspondence between 
acoustic signals and phonemic categories. Acoustic signals for the same phonemic 
category vary by speaker, prosodic tone, and context. Though acoustic signals are 
different in these different conditions, the same phonemic percept is perceived. 
Conversely, the exact same acoustic signal under different contexts can yield different 
phonemic percepts. The lack of a relationship between acoustic signals and phonemic 
categones suggests that acoustic signal by itself is not sufficient for the perception of 
speech. Furthermore, that visual feedback of oral gestures can influence the perception 
of a speech sound (McGurk & MacDonald, 1976; MacDonald & McGurk, 1978) 
suggests that both visual and acoustic signals are merely cues for the object of perception. 
Liberman and Mattingly (1985) argued that the object of speech perception is the 
intended gesture, or the actual motor movement, of the speaker. The object is the 



3 

inlende J gesture because much of the gesture takes place inside the speaker's oral cavity 

and out of sight of the perceiver. 

These motor theorists propose that the speech perception system is able to decode 
intended gestures from auditory signals because speech perception system is a specialized 
neural module evolved to perform such linguistic functions. They assumed that the 
development of motor control over the vocal tract preceded the evolution of speech. 
Adaptations made coarticulation of rapid phonetic gestures possible. A perceiving 
system developed concomitantly, and this system is specialized to take into account 
complex acoustic consequences. Because the perception system developed 
concomitantly with the production system, they are biologically linked. The calculations 
necessary to perceive speech are done automatically via hardwired neural structures that 
connect the production and the perception parts of the system. This system is one 
specialized for linguistic functions. It is a linguistic module that operates independently 
from the general auditory system that processes other non-linguistic signals. 

Heilman's Motor-Articulatorv Feedback Hvp othesi.s 

Heilman, Voeller, and Alexander (1996) elaborated on Liberman's theory and 
proposed a motor-articulatoiy feedback hypothesis of speech perception. They 
emphasized that the perception of spoken words is associated with the production of 
intended articulator gestures. As an infant learns to perceive words, s/he imitates the 
sounds heard by replicating intended articulatory gestures of the speaker with his/her 
articulators. As the infant fine-tunes this imitation of sounds, s/he associates each 
phoneme with a movement of his/her articulators. Feedback from the articulators to the 



own 



4 

neural module is essential for the individual to be aware of this relationship between 

phoneme and articulatorv' gesture. Understanding of this relationship constitutes 
articulatory knowledge, which facilitates parsing spoken words down into phonemic 
parts. 

Heilman et al. (1996) applied their theory to the problem of grapheme-to- 
phoneme conversion in reading. They pointed out that when first learning to read, one 
needs to break words down into letters clusters and associate them with their respective 
phonemes. The motor-aniculator>' feedback hypothesis posits that children use the 
articulatory apparatus when learning to associate specific graphic representations with 
phonemic representations. Learning to read includes using the articulatory knowledge 
learned earlier to associate phonemes with graphemes. Without this articulatory 
knowledge as a mediating tool, the means by which written words are broken do^vn into 
letter clusters may seem arbitrary. Reading thus involves associating already established 
phoneme-articulatory relationships to graphemic representations. Beginning readers 
often move their lips and tongue even when reading silently. Adults also engage the 
articulators when reading novel or hard-to-pronounce words. These findings suggest that 
engaging the articulators facilitates reading. The motor-articulatory feedback hypothesis 
proposed that it is the feedback that articulators provide which facilitates the learning of 
grapheme-to-phoneme conversion. 

Anatomy of the Articulatory Feedback .Sy <;tpm • 

Central to the human language system are two major neural regions, Wernicke's 
and Broca's areas in the left cerebral hemisphere of most nght-handed individuals (Kolb 



5 

& Whishaw, 1990). Wernicke's area, which is located in the left postenor portion of the 

superior temporal gyrus, and surrounding regions are sometimes referred to as posterior 
language areas that process the perception of language. Classically, a lesion in 
Wernicke's area results in fluent aphasia, characterized by fluent speech but impaired 
comprehension, repetition, and naming. Information from these posterior perisylvian 
regions travels anteriorly to Broca's area via the arcuate fasciculus. Broca's area, located 
m the left inferior frontal gyrus, is conceptualized as an area specialized for motoric 
programming of speech. Lesion of Broca's area results in nonfluent aphasia characterized 
by intact comprehension but effortful, nonfluent speech. 

The motor-articulatory hypothesis proposed by Heilman et al. (1 996) emphasizes 
the learned association beUveen a phoneme and an articulatory gesture. When an infant 
hears a novel word, his/her auditory and auditory association cortex (Wernicke's area) 
analyze the sounds in the word. Pars operculans, tnangulans, and the foot of the motor 
cortex (Broca's area) execute a complex motor program to approximate the heard word. 
Primary motor cortex activates the articulators in the oral cavity. During word imitation, 
as the articulators move, they send sensory feedback (i.e., propnoceptive and tactile) to 
the primary sensory cortex and sensory association cortex. These sensory cortices 
presumably connect with the frontal areas involved in motor planning (e.g., Broca's area), 
thus providing linkage back to the articulatory system. The sensory areas also project to 
polymodel sensory cortex in the temporal-parietal region that stores auditory 
representations of words (e.g., Wernicke's area). Eventually, through this connectivity, 
the infant learns that each phoneme is associated with one motor pattern for articulation, 
and that words are associated with a series of articulatory patterns. 



Proposed Hypothesis: Articulatorv Feedback Hypothesis of Naming 

Articulatory knowledge assists reading presumably because it facilitates retrieval 
of information. In the case of reading novel words where it is necessary to use 
grapheme-to-phoneme conversion, the subject sees a grapheme, and this grapheme 
triggers the phoneme associated with it and the articulatory motor pattern used to produce 
that phoneme; execution of this motor pattern results in the production of the phoneme 
associated with the target grapheme. Movement of the articulators may not be necessary 
for the retrieval of the phoneme, and feedback may even be intracerebral. 

If articulatory feedback assists reading, it is likely that it also assists name 
retrieval, a more basic language function that came into use much earlier in the 
evolutionary process than reading. The articulatory feedback hypothesis of naming states 
that articulatory feedback facilitates name retrieval. It does not state that articulatory 
feedback is necessary for naming or sufficient for naming. Rather, articulatory feedback 
assists other retneval systems, making the process faster and more efficient. 

Many of our everyday experiences suggest that activation of motor patterns can 
facilitate retrieval. The tip-of-the-tongue phenomenon, where one experiences problems 
retrieving a word, may sometimes be overcome by articulating the beginning sound. 
When one cannot recall a phone number, pretending that one is dialing, thereby engaging 
the motor system controlling the fingers, sometimes assist the recall of phone numbers. 
Whereas retrieval may not need motor activation, if some part of the retneval system is 
compromised, motor activation may provide the extra input necessary to activate a 
representation. 



7 

Heilman et al. ( 1 996) posited that in order to benefit fi-om articulator feedback, 

one must have articulatory knowledge. Articulatory knowledge refers to the ability to 
locate the position of the articulatory structures, such as the tongue and the lips, during 
phoneme production. This knowledge could be conceptualized as a neural association 
between the articulatory system, the sensory/proprioceptive system, and the phonemic 
representation system. Without this link, activation of the articulators may not coactivate 
neural patterns representing phonemic percepts, which would limit the articulatory 
system's ability to facilitate retrieval. The spontaneous use of articulatory knowledge will 
be referred to as articulatory awareness. 

Montgomery (1981) showed that children with dyslexia have impaired articulator 
knowledge compared to children without dysle.xia. She presented cartoons of sagittal 
drawings through the oral cavity that illustrate the position of tongue, teeth, and lips, then 
asked which of the cartoons matched phonemes that she produced. She encouraged the 
subjects to repeat the phonemes as much as they want and to think about the location of 
their tongue, teeth, and lips. The non-dyslexic children were able to correctly identify 
their articulatory positions better than dyslexic children. All children were able to repeat 
the phonemes; thus the dyslexic children's deficit cannot be explained by an auditory 
perceptual deficit. They appeared to be unknowledgeable about the position and 
movemem of their articulators. They lacked articulatory knowledge. 

Montgomery's (1981) work suggested that dyslexic children lack sensory 
feedback about their articulators' position and movement. This population could be 
instrumental in testing the articulatory feedback hypothesis of naming. The hypothesis 
stated appropriate articulator movements (and the sensory feedback concomitant with 



W. 



8 

those movements) facilitate lexical retrieval in individuals with articulatory knowledge. 

In the normal population, inhibiting appropriate articulator movements while asking 
subjects to name objects should impede their naming. This was done in this study by 
introducing an interference task. Subjects were asked to engage in mouth movements 
that interfered with the articulation of object names. Thus the articulatory system could 
send articulatory feedback to the central nervous system that was appropnate to and 
facilitated name retrieval. Individuals with impaired articulatory knowledge should 
respond differently. They could differ in one of two ways. First, because these 
individuals may not be as knowledgeable of their articulator movemems as normal 
individuals, they may not use an articulatory strategy to assist naming. Therefore 
interfering with the articulators dunng a naming task may not impede the naming process 
of these individuals as much as it does in controls. However, individuals with impaired 
articulatory feedback may have no other alternative strategy to assist naming. Because 
they already have impaired articulatory feedback, addmg an interference could impair 
their naming ability even more, making them less capable of retrieving names than 
controls. 

Developmental Phonological Dvslexia 
Definition o f Develop me ntal Phonological Dvslexia 

The terms dyslexia, learning disability in reading, and reading disability have 
been used interchangeably in the literature. All three refers to problems with reading, but 
a clanfication of terms is in order. The term.dyslexia has been used in research 
attempting to understand the neurological or neuropsychological deficits underlying the 



disorder. Dyslexia can be categorized into developmental versus acquired. Acquired 

dyslexia refers to those individuals who acquired the disorder through insults to the 

central nervous system after a period of normal reading development (Coslett, 1997). 

Developmental dyslexia refers to those individuals who demonstrate problems in the 

development of reading. Researchers have posited the existence of many types of 

dyslexia, including phonological, surface, and deep dyslexia, to name a few (Ellis & 

Young, 1988). The Orton Dyslexia Society Research Committee has defined dyslexia as: 

one of several distinct learning disabilities. It is a specific language-based 
disorder of constitutional origin characterized by difficulties in single word 
decoding, usually reflecting insufficient phonological processing abilities These 
difficulties in single word decoding are often unexpected in relation to age and 
other cognitive and academic abilities; they are not the result of generalized 
developmental disability or sensory impairment. Dyslexia is manifest by variable 
difficulty with different forms of language, often including, in addition to 
problems reading, a conspicuous problem with acquinng proficiency in writing 
and spelling. (Shaywitz, Fletcher, & Shaywitz, 1995, p. S51) 

This committee, composed of representatives from the National Institute of Child Health 

and Human Development, defined dyslexia as a subtype of learning disability. Thus 

learning disability is an umbrella term encompassing many types of problems with 

learning, including reading and math. Reading disability is an abbreviated term for 

learning disability in reading. This is a legal term used to identify individuals who meet 

legal critena to receive special education services. Dyslexia is used interchangeably with 

reading disability, but it is a theoretical/research term, and its use implies neurological 

abnormalities within the language system that underlie difficulty with reading processes. 

Dyslexia constitutes 80% of children diagnosed with learning disability 

(Shaywitz, Fletcher, & Shaywitz, 1995). Of the different types of dyslexia, phonological 

dyslexia is the most common form. Phonological dyslexia refers to reading problems 



■i -.< 



:t;ft^' 



10 

secondary to phonological processing deficits. Specifically, impairment in the 

grapheme-to-phoneme conversion system has been implicated. A simplified version of 
Ellis and Young's (1988) model for oral reading is depicted in Figure 1 . According to 
this model, written words are processed initially by the visual system, which processes 
visual stimuli by analyzing each individual letter component. After visual analysis, 
reading can be achieved by three mechanisms or routes: I) Results of the visual analysis 
system enter into the orthographic input lexicon, which contains visual representations of 
words an individual has learned. The selected lexical representation enters into the 
semantic system and activates the meaning of the word. From the semantic system, the 
appropriate auditory representation of the word is activated in the speech output lexicon. 
Then speech is produced by activation of motor patterns. Most proficient readers are 
thought to use this lexical system because of its efficiency and completeness compared to 
the other two systems. 2) The second method for reading is similar to the first except that 
the semantic system is bypassed, so that words can be read without accessing the 
meaning of words. These two lexical, or whole-word reading routes are important for 
reading irregular and ambiguous words, and cannot be used to read nonwords or 
pseudowords. 3) The third method, the phonological route, is a labor-intensive system 
used when reading novel words (i.e., words without representation in the orthographic 
input lexicon). The visual system analyzes words and parses words imo letter 
components. The letter or letters (grapheme) are converted to the sounds they represent 
(phoneme). The phonemes are blended to produce the phonological sequence for the 
entire word. Phonological dyslexia results when this grapheme-to-phoneme conversion 
link is defective. 



11 



Semantic 
System 



Speech 
Output 
Lexicon 



Written word 



1 



Visual 

Analysis 

System 



Visual 

Input 

Lexicon 




Grapheme-Phoneme 
Conversion 



Phoneme 
Level 



Speech 



Figure I. A simplified model of oral reading fi-om Ellis and Young (1988). 



12 

One problem with dyslexia research is that research groups often do not specify 

the type of dyslexia subjects demonstrate. This is especially true with treatment-focused 
research or service, where the primary goal is to improve patients' reading skills, 
regardless of whether patients meet criteria for certain theoretical subtype of dyslexia. 
This was true for Montgomery's ( 1 98 1 ) work, which reported that subjects were 
"dyslexic" without specification of subtype. The present study, in an attempt to strive for 
theoretical clarity, will limit dyslexic participants to those with developmental 
phonological dyslexia, defined as individuals who have impaired grapheme-to-phoneme 
conversion, because this is the largest, most common subtype of dyslexia. Thus an 
assumption of the present study is that a large percentage of participants in the 
Montgomery (1981) study were phonological dyslexics, and that phonological dyslexics 
have decreased awareness of their articulator position and movement. 

The identification of developmental phonological dyslexia is problematic for two 
reasons. First, dyslexia falls under the broad category of specific learning disability 
under Individuals with Disabilities Education Act (IDEA, 1997; Public Law 105-17), 
which requires that educational institutions provide special services to meet the 
educational needs of individuals with disabilities. Because the law does not require the 
specification of the subtype of reading disability, most clinical organizations do not 
specify if a patient's reading disability- fits the phonological subtype in diagnostic 
evaluations. Second, the IDEA specified that reading ability should be discrepant from 
intellectual aptitude, but it did not state how such discrepancy should be measured. 
Different groups have used intelligence quotient (IQ)-achievement discrepancy 
(Ackerman & Dykman, 1993; Cornwall, 1992), chronological age-reading age 



13 

discrepancy (Fawcett & Nicolson, 1994; Felton, Naylor, & Wood, 1990; Felton, Wood, 

Brown, Campbell, & Harter, 1987; Wolf & Goodglass, 1986; Wolf & Obregon, 1992), 
arbitrary cutoff scores on tests of achievement or based on teacher/school referrals 
(Bowers & Swanson, 1991 ; Denckla & Rudel, 1976; Korhonen, 1995; Manis, 
Seidenberg, Doi, McBride-Chang, & Petersen, 1996; Mattis, French, & Rapin, 1975; 
Swan & Goswami, 1997), or regression approaches that control for the intercorrelation 
between achievement and IQ measures (Fletcher, Francis, Rourke, Shaywitz, & 
Shaywitz, 1992; Fletcher, Schaywitz, Shankweiler, Katz, Liberman, Stuebing, Francis, 
Fowler, & Shaywitz, 1994; Pennington, Gilger, Olson, & DeFries, 1992; Shaywitz, 
Escobar, Shaywitz, Fletcher, & Makuch, 1992; Shaywitz, Fletcher, Holahan, & Shaywitz, 
1992; Shaywitz, Shaywitz, Fletcher, & Escobar, 1990). Consequently, the literature on 
this population is fraught with inconsistent diagnostic criteria for reading disability. 

The IQ-achievement discrepancy method has been shown to be problematic in 
diagnosing reading disability among minority populations. Duckworth (1999) showed 
that among a sample of college students referred for evaluation of learning disability, 
African Americans score on average 12 points lower on the Wechsler Adult Intelligence 
Scale-Revised (WAIS-R) intelligence quotients than their European-American 
counterparts. Although psychologists have attempted to design culture-fi-ee intelligence 
tests in recent years, Duckworth's data suggest that a commonly used intelligence 
measure, WAIS-R, is still biased against minority populations. The IDEA specified that 
learning disability cannot be due to mental retardation, which is defined as IQ scores of 
below 70. If the normal distribution of Afincan Americans' IQ scores is downshifted by 
12 points, then the difference between the mean IQ score of the African-American 



14 

population and the mental retardation cutoff of 70 is decreased by 12 points, which in 

effect, decreases the potential number of African Americans who can meet diagnosis for 
learning disability using a simple difference discrepancy method. 

An alternative to this problem is to calculate expected achievements scores based 
on intellectual aptitude via a regression method. This method controls for the inter- 
correlation between achievement and intelligence measures and the regression of 
achievement scores toward the mean intelligence score, and minimizes problems of over- 
identifying high-IQ subjects and under-identifying of low-IQ subjects as learning 
disabled. Using the regression formula, 
Y' = [r^.(S,/Sy)(IQ-X)] + Y 
(where Y' is the expected achievemem score for a given IQ, r,y is the correlation between 
the IQ and the achievement test, S, is the standard deviation of the achievement test, Sv is 
the standard deviation of the intelligence test, IQ is the achieved intelligence score, X is 
the mean of the intelligence test, and Y is the mean for the achievement test), Duckworth 
showed that among those referred for a learning disability evaluation, more African 
Americans would be classified as learning disabled than using a simple discrepancy 
difference method (5]o/o vs. 28%, respectively), while the method of classification used 
does not significantly affect the number of European Americans classified as learning 
disabled (27% vs. 30%, respectively). 

Nature and Extent of Naming Deficit 

A basic question relates to the existence oUonafide naming deficits in the 
dyslexic population. Because dyslexia is a reading disability, could their naming deficits 



15 

be attributable to a lack of vocabulary? If they do have bona fide naming problems, do 

they have problems retrieving names of symbols that compose written language? Or do 
they have a general retrieval problem that implicates naming of other targets? Fawcett 
and Nicolson (1994) examined naming performance of 35 dyslexic children (defined as 
havmg at least an 1 8 month discrepancy between chronological and reading age, with full 
scale IQ of at least 90) and 32 chronological age conttols (CA). They found that 
dyslexics have impaired naming of letters, digits, colors, and picttires compared to the 
CA group, with the picture naming task being the most robust measure differentiating 
groups. The dyslexics' discrepant performance on color and picture naming suggested 
that their deficit was not limited to grapheme-to-phoneme translation. They have acttial 
problems with name retrieval. 

A critique of Fawcett and Nicolson's ( 1 994) sttidy was that their groups were not 
matched on intellectual aptitude and therefore retrieval differences may be explained by 
intelligence differences between dyslexics and controls. Swan and Goswami (1997) 
recrutted a dyslexia group (n = 16), a CA control group (n = 16), a reading age (RA) 
contt-ol group (n = 16), and a garden-variety poor reader confrol group (GV; n = 16). All 
groups had matching IQ scores except the GV (101-105 vs. 79), and all groups had 
matchmg reading age except the CA (1 12-1 16 mo. vs. 139 mo.). Swan and Goswami 
(1997) found the following pattern of performance on a picttire naming test (percentage 
correct score based on the total number of items familiar to each subject): 
CA>RA>GV=dyslexics. Dyslexics performed as well as GV. Both groups' naming 
scores were worse than younger but reading age matched controls (RA). All of these 
groups performed worse than the CA controls. However, dyslexics were more accurate 



16 

than any other group in correctly recognizing targets on a follow-up multiple choice task 

composing of items they failed to name spontaneously. (Subjects' scores on this test were 
coded as a proportion of their total error score, so scores were not inflated for those with 
more errors). Dyslexics' ability to correctly identify targets in a recognition paradigm 
argued against a vocabulary deficit. Rather, it supported that they have problems of 
retneval. In contrast, GV were found to have poorer vocabulary on a test of receptive 
vocabulary. Swan and Goswami (1997) concluded that while GVs poor picture naming 
performance was due to poor vocabulary, dyslexics' was due to problems with name 
retrieval. 

Wolf and Obregon (1992) found similar results using a multiple-choice paradigm 
with items on the Boston Naming Test (BNT) that were missed. Their selection cnterion 
for dyslexia was better defined than the Swan and Goswami ( 1 997) study: Dyslexics 
were 2 or more years below expected reading level as assessed by the Gray Oral Reading 
Test. Compared to an average reader control group (n = 42), dyslexics' (n = 8) naming 
was worse, but dyslexics were more accurate on identifying the correct target in a 
multiple-choice format compared to controls. They also concluded that dyslexics' 
naming errors were reflective of a retrieval deficit. 

These studies showed that dyslexics have lexical retneval problems on formal 
neuropsychological tests. Murphy, Pollatsek, and Well (1988) questioned 1) if dyslexics' 
retneval problem was one of general processing deficit or was it specific to language, and 
2) whether dyslexics' retrieval deficit can be seen in their natural/spontaneous use of 
language. They reasoned that if dyslexics have a general processing deficit, they should 
be slower on tasks not involving the explicit use of language, such as a simple reaction 



17 

time task requiring them to move their finger to the side where a visual target appeared, 

and on a picture categorization task requiring them to indicate if a picture is an exemplar 
of a target category. If their retrieval deficit was specific to language, they should show 
deficient performance on tasks of oral expressive and receptive language as well as on 
formal neuropsychological measures. They tested dyslexics identified by poor Rapid 
Automatized Naming (RAN) performance and who were at least two years below their 
expected reading level (n = 14). Controls were matched for age and IQ (n = 14). They 
found no difference between groups on basic motor reaction time and picture 
categorization, which ruled out the general processing deficit hypothesis. Dyslexics 
performed worse than controls on both formal (BNT) and informal language measures. 
On informal, expressive language measures, dyslexics generated fewer words in retelling 
stones and had slower verbal output. On informal, receptive language measures, they 
were slower at categorizing spoken words. The authors concluded that dyslexics' name 
retrieval problem reflected a specific linguistic deficit, and not a general processing 
deficit, and their name retrieval problem manifested in their oral language as well as on a 
formal neuropsychological measure. 

The retneval problems that dyslexic children demonstrate in childhood have been 
shown to persist imo adulthood. Korhonen (1995) followed a small group (n = 8) of 
children who had problems in rapid automatic naming and in word retrieval, and tested 
them approximately 9 years later at 18 years of age to examine the persistence of naming 
deficits identified during childhood. These children were onginally identified by their 
teachers as learning disabled children who demonstrated special problems in reading. 
Korhonen comparing these individuals' performance to controls matched on age, sex, IQ, 



18 

parent SES at nine years of age, and education level at 1 8 years of age (n = 10). 

Korhonen found that learning disabled individuals were slower and made more errors on 
rapid color naming and rapid object naming, and on another test of rapid alternating 
stimulus naming. The findings were not as robust as at nine years of age; nevertheless 
they were present. Fawcett and Nicolson ( 1 994) tested dyslexics from eight to 1 7 years 
of age and also found naming deficits in their 17-years old dyslexic group (n = 13). 
Felton, Naylor, and Wood (1990) followed 1 15 children %vith dyslexia into adulthood. 
They defined dyslexia as a discrepancy of 1 .5 years between chronological and reading 
age. They found persistent problems in rapid naming, nonword reading, and 
phonological awareness. These findings of persistent naming problems suggested a 
deficit model of dyslexia, which conceptualized dyslexia as a deficit that does not "catch 
up" with maturation. 

Role of Phonological Awareness 

A hypothesized deficit underlying dyslexia is an impaired sense of phonological 
awareness (Liberman & Shankweiler, 1985). Swan and Goswami (1997) used a picture 
naming paradigm to study the role of phonological processing in dyslexics. They 
hypothesized that if a phonological deficit underlies dyslexia, dyslexics' naming 
performance would be worse for longer words of low frequency. Longer words have 
more phonemes to encode and retrieve, and thus were more demanding on the 
phonological system. Low fi-equency words occur less often in language, making them 
less familiar to the phonological system. They found a Group X Frequency X Length 
interacfion, where with frequency controlled, dyslexics (n = 16) named short words better 



19 

than long words. This pattern was not seen in the CA, RA, or GV controls. Lower level 

interactions also showed expected findings: Dyslexics named short words better than 
GV, but their naming of long words was worse than GV and RA. Dyslexics named high 
frequency words better than GV, but their naming of low frequency words was worse 
than RA. Swan and Goswami ( 1 997) further posited that dyslexics' picture naming 
would be worse than word naming because in word naming, letters were available to 
assist the phonological system. In picture naming, no cues were present to assist the 
phonological system. They did find impaired picture naming compared to word naming 
for dyslexics but not for RA and CA. 

A natural question that arose with evidence of phonological and naming deficits 
in dyslexia regards the relationship between these processes. Two studies have addressed 
this issue but with incongruent results. Cornwall (1992) used a regression analysis to 
examine if phonological awareness and rapid automatized naming contributed unique 
variances to reading disabled children's scores on academic achievement (n = 54; reading 
disability was defined by >= 16 standard poim discrepancy between Wide Range 
Achievement Test, Revised Reading subtest and WISC-R FSIQ, with WISC-R FSIQ >= 
90). If phonological awareness and rapid automatized naming contributed unique 
vanances, then they were likely independent processes affecting the dyslexic population. 
With age, SES, behavioral, and intelligence factors controlled, she found that 
phonological awareness (as assessed by Auditory Analysis Test [AAT], a phonemic 
deletion test) and rapid naming did contribute unique shares of vanance to achievement 
scores. Phonological awareness contnbuted to nonword reading, spelling, and 
comprehension. Naming contributed to single-word reading and passage reading speed. 



20 

Bowers and Swanson (1991) also conducted regression analyses to examine the same 

issue. They found that most variance on nonword reading (after controlling for the 
WISC-R Vocabulary score) was explained by the score on the Auditory Analysis Test, 
and most variance on a single-word reading test was explained by the score on Odd Word 
Out, another phonological awareness measure (a multiple-choice test requiring 
identification of the non-rhyming word). In contrast, most variance on comprehension 
was explained by rapid automatized naming A problem with Bowers and Swanson's 
(1991) study was that they combined poor readers (n = 19; defined by Woodcock 
Reading Mastery Test, Word Idemification subtest standard score at or below the 25* 
percentile for age) with average readers (n = 19) in their regression analyses. It was 
possible that subjects with differem reading abilities have differem patterns of 
relationship between phonological awareness and naming. That is, phonological 
processes and naming abilities may contribute differently to the reading achievement 
scores of average and disabled readers. The lack of well-controlled studies in this area 
rendered the contributions of phonological awareness and naming to reading achievement 
equivocal. 

Anatomical Evidence of Anomalies 

Galaburda and colleagues (Galaburda, Sherman, Rosen, Aboitiz, and Geschwmd, 
1985; Galaburda, 1989) examined eight post-mortem brains of individuals identified by 
the Orton Society as dyslexic. They found abnormal symmetry of the planum temporal 
in these eight brains as well as ectopic neurons in the molecular layer of the perisyl 
cortex. The planum temporale lies just postenor to the Heschl's gyrus on the superior 



e 
vian 



21 

surface of the temporal lobe. These and other structures surrounding the Sylvian fissure 

compose the language system, which includes reading. The findings of Galaburda et al. 
(1985) suggested that neurodevelopmental abnormalities may contribute to the 
symptomatology of dyslexia. 

Geschvvind and Levitsky (1968) found that among 100 post-mortem samples, 
approximately 65% showed a left greater than right plana difference. Approximately 
25% had symmetrical plana, and only 10% had a right planum that was larger than the 
left. Rumsey, Dorwart, Vermess, Denckia, Kruesi, and Rapoport (1986) measured 
temporal lobe volume from magnetic resonance (MR) images and found data consistent 
with the results of Galaburda et al. (1985). Nine of the ten men with documented reading 
disability in psychoeducational evaluations from their childhood demonstrated 
symmetrical temporal lobes. However, Rumsey et al. (1986) did not measure the planum 
temporale specifically. Given the extent of the temporal lobe, it was possible that other 
aspects of the temporal lobe contributed to the symmetry rather than the planum 
temporale. 

In 1990, two independem groups reported on the symmetry of the planum 
temporale among dyslexics. Hynd, Semrud-Clikeman, Lorys, Novey, and Eliopulos 
(1990) compared plana length and insular length of 10 developmental dyslexic children 
with 10 non-dyslexic controls. The average age of their dyslexic children was lOyears, 
and dyslexia was defined by normal or better intellectual ability (WISC-R Full Scale IQ 
>= 85), reading achievement significantly below their FSIQ (>= 20 standard score points 
lower than FSIQ on Woodcock Reading Mastery Test-Revised, Word Attack and 
Passage Comprehension subtests), and no co-morbid diagnosis of ADHD. The average 



22 

age of their normal controls was 12 years, and they must have normal or better 

intellectual ability (WISC-R FSIQ >= 85), no reportedly family history of learning 
problems, no significant deficit in achievement, and no reported or observed medical, 
educational, social, or emotional difficulties. From MR images, this research group 
found that dyslexics have bilaterally shorter insula compared to non-dyslexic controls, 
and that 90% of dyslexics have a left planum length that was shorter than their right 
planum length. There was no difference between dyslexic and control groups on right 
planum length. Dyslexics' overall left planum was shorter than controls' left planum. 
They suggested that the nature of plana symmetry in dyslexia was due to a smaller left 
planum temporale. Larsen, Hoien, Lundberg, and Odegaard (1990) also examined plana 
length from MR images and found symmetrical plana among dyslexic children (n = 19; 
dyslexic subjects were identified from a school psychology service and had poor word 
recognition in the presence of normal intelligence). However, comparing dyslexics' MR 
images to those of age- and intelligence-matched controls' (n = 19), they found that plana 
symmetry among dyslexics was due to increased right planum length rather than the 
decreased left planum that Hynd et al. ( 1 990) reported. 

The above studies did not differentiate between the temporal and parietal banks of 
the planum temporale. Leonard, Voeller, Lombardino, Morris, Hynd, Alexander, 
Andersen, Garofalakis, Honeyman, Mao, Agee, and Staab, (1993) suggested that 
examining the different banks of the planum may explain some of the contradictions in 
the literature. They measured the length of these two banks from MR images. Their 
subjects were adults previously diagnosed with dyslexia by pediatrician, pediatric 
neurologist, or learning disability specialists (n = 9), the dyslexics' biological relatives (n 



23 
- 10), and normal controls (n = 12). In contrast to the previous studies, Leonard et al. 

(1993) did not find abnormal symmetrical plana in the dyslexic population. All groups 
demonstrated a greater left temporal bank compared to the nght temporal bank and a 
greater right parietal bank compared to the left parietal bank. When only the left 
hemisphere was considered, all subjects except two dyslexics had longer temporal bank 
than the parietal bank. When only the nght hemisphere was considered, the controls also 
had longer temporal bank than the parietal bank, but 55% of dyslexics and 40% of 
relatives had longer parietal bank compared to the temporal bank. They suggested that 
dyslexics had reduced right intrahemisphenc asymmetry (bet^veen temporal and parietal 
banks) compared to controls due to the transfer of planar tissue from the temporal to the 
parietal bank. 

The same group also examined the structure of the Sylvian fissure. Among 
controls, the left Sylvian fissure usually ended in a bifurcation into small ascending and 
descending branches. Variations to this typical pattern included no bifurcation and/or 
extra gyri in the parietal operculum anterior to the termination of the Sylvian fissure. 
There were also vanations in the number of Heschfs gyn present. Normally, there was 
one Heschl's gyrus in the left hemisphere that was visible on a mesial section of the MR 
image. On more lateral sections, Heschl's gyrus moved antenorly and dissolved into a 
number of convolutions in the superior temporal gyrus. Leonard et al. (1993) found that 
every subject with dyslexia showed at least one of the above anomalies. Six (66%) had 
bilateral anomalies. Biological relatives had the next highest number of anomalies; 
seventy percent had at least one anomaly while 20% had bilateral anomalies. In contrast, 
only 17% of control subjects had one anomaly and none had bilateral anomalies. These 



24 

findings suggested a genetic etiology- for dyslexia. The greatest number of anomalies 



was 

found among dyslexics, the group with the next greatest number of anomalies was 
biological relatives of the dyslexics, and normal controls without family histories of 
dyslexia had the fewest number of anomalies. These anatomical studies indicated that 
reading difficulties experienced by dyslexics may have an anatomical basis. However, a 
word of caution regarding the Leonard et al. (1993) study is in order. Their subjects were 
either professionals or from high functioning professional families. They described their 
dyslexic subjects as "recovered dyslexics" who have been able to compensate so well that 
there was much overlap between dyslexic and control groups on a measure of 
phonological awareness. Thus their dyslexic subjects may not be a representative sample 
of the dyslexic population. 

Hynd et al. (1990), in addition to finding shorter left planum length among 
dyslexic children, also compared the MR images of dyslexics (n = 10) with MR images 
of children with attention deficit^yperactivity disorder (ADHD; n = 10). Their ADHD 
subjects had average or better intellectual ability (WISC-R FSIQ >= 85), no reported 
family history of learning problems, no significant deficit in reported or measured 
achievement, documented behavioral deficits consistent with a Diagnostic and Statistical 
Manual of Mental Disorders, Revised Third Edition (DSM-III-R) diagnosis of ADHD, 
who responded favorably to stimulant medication. They found that dyslexics and 
ADHDs both have smaller nght frontal width compared to controls. However, dyslexics' 
planum temporale was shorter on the left while ADHDs showed the typical pattern of left 
greater than nght planum. These findings showed that while frontal anomalies may be 



25 
implicated in both groups, anomalies of the planum temporale may be specific to 

dyslexia. 

Imaging data from cerebral blood flow studies also supported the evidence of 
structural anomalies in the dyslexic population. Rumsey, Andreason, Zametkin, Aquino, 
King, Hamburger, Pikus, Rapoport, and Cohen (1992) examined cerebral blood flow 
differences between dyslexic adults and normal subjects dunng a rhyming task. Their 
dyslexic subjects all had Wechsler Adult Intelligence Scale-Revised (WAIS-R) Verbal 
or Performance IQ scores of at least 89 and met DSM-III-R critena for developmental 
reading disorder. All received some special education service while in school. Subjects 
were presented word pairs aurally and pressed a button if the word pair rhymed. They 
found that dyslexics had decreased activation of left temporal-parietal and midtemporal 
areas that corresponded to the angular gyrus and Wernicke's area. This finding of 
hypometabolism in temporal parietal and midtemporal areas corresponded well to the 
structural abnormalities of planum temporal and Heschl's gyrus reported by Leonard et al. 
(1993). 

Paulesu, Frith, Snowling, Gallagher, Morton, Frackowiak, and Frith, (1996) 
conducted a different rhyming task during a positron emission tomography (PET) study 
and found similar results. Their rhyming task involved visual presentation of letters. 
Subjects moved a joystick to letters that rhymed with the letter "B." Their subjects were 
five dyslexic adults who were university students, postgraduates, or self-employed 
entrepreneur, identified fi-om records of a dyslexia clinic, and five education-matched 
controls. The non-dyslexic subjects activated left Broca's and Wernicke's areas and the 
left insula. Dyslexics showed decreased activation in the left Wernicke's area and a 



26 

greater decrease in the left insula. On a short-term memory task where subjects judged if 

a target letter was present in a previous sequence of English letters, the normals activated 
the above areas plus the left supramarginal gyrus. The dyslexics activated the same areas 
as the controls except for the left insula. On these two tasks, dyslexics activated the same 
major language areas as controls (i.e., Broca's and Wernicke's) while attending to and 
judging phonological stimuli. However, they did not activate these areas in concert as 
controls. Dyslexics' lack of activation of the insular cortex suggested that the insula was 
not necessary for phonological processing. The authors suggested that perhaps the insula 
acted as a "bndge" between the Broca's area and the supramarginal gyrus. Though it may 
not be necessary for the processing of phonological information, it provided the 
connection between postenor and antenor regions. Dyslexics' anatomical anomalies and 
their lack of activation of this region during phonological analysis tasks suggested that 
disconnection between important regions for phonological analysis may underlie their 
problems with phonological processing. 

Co-morbiditv with Attenti on-Deficit Hvperactivitv DisnrHpr 

While reading disability and ADHD have very different symptoms, they do 
overlap much more than one would expect from independem random distributions of 
these disorders. Approximately 30-50o/o of individuals with reading disability have a co- 
morbid diagnosis of ADHD (Felton et al., 1987). This high co-morbidity rate has led 
researchers to speculate if attentional problems limit a child's ability to develop 
automated processing skills necessan- for reading. Felton et al. (1987) aimed to 
disentangle the neuropsychological deficits contnbuted by attemion deficit disorder 



27 

(ADD) and by reading disability (RD). They formed four groups from two factors, RD 

and ADD: RD with ADD, RD with non-ADD, non-RD with ADD, and non-RD with 
non-ADD. Using age and receptive vocabulary score as covariates and controlling for 
family-wise error rates, Felton et al. (1987) found that RD and non-RD groups differed 
on a visual confrontation naming test (BNT) and on a rapid automatized naming test. 
There was no main effect of ADD on these measures. The ADD and non-ADD groups 
did differ from each other on a test of supraspan verbal memory (RAVLT). In contrast, 
there was no main effect of RD on this task. These findings showed that RD and ADD 
contributed to different aspects of neuropsychological deficits. If ADD contributed to 
impaired reading skills among the RD children, one would expect some overlap of 
impaired areas. The findings of Felton et al. (1987) provided indirect support for the 
independence of reading disability and attention deficit disorder. 

Research Questions 

The literature on the dyslexic population indicated that 1) dyslexic individuals 
have problems with name retrieval; 2) dyslexic individuals have problems with 
phonological processmg; and 3) their language difficulties likely have an anatomical 
basis. This combination of findmgs rendered the phonological dyslexic population to be 
of special interest to this study, because the anatomical areas identified to be abnormal 
(i.e., Wernicke's area) were also implicated by the articulatory feedback hypothesis of 
naming. This hypothesis posited that articulatory awareness facilitates naming. 
Presumably, sensory feedback received by the primary sensory cortex from articulators 
has connectivity with both the Wernicke's area and Broca's area. This comiectivity 



28 

allows for articulatory feedback to trigger phonological representations of object names, 

and to trigger motor patterns to execute the articulation of those names. Phonological 
dyslexic subjects and normal readers should yield a range of naming abilities by which to 
examine articulatory knowledge and the relationship between name retrieval and 
articulatory knowledge. The aim of this study was to test the articulatory feedback 
hypothesis of naming. To achieve this aim, the following questions were asked: 

What Is the Correlation Between Articulatory Knowledge and Naming? 

If articulatory knowledge and naming are related, better articulatory knowledge 
should be associated with either faster name retrieval latency or better name retneval 
accuracy. This relationship should hold for all subjects. Reading achievement status 
may put subjects at the lower end of the continuum of naming ability. If articulatory 
feedback facilitates naming for all subjects, dyslexics' naming ability will be correlated 
with their articulatory knowledge in the same manner as normal readers. 

Do Dvslexics Have Worse Articulator/ Knowledge? 

A secondary aim of this study was to replicate Montgomery's (1981 ) finding that 
dyslexics have impaired articulatory knowledge. This study differed from Montgomery's 
(1981 ) study in some respects. One, it was unclear how Montgomery's dyslexic subjects 
were defined. This study included only those who have impaired phonological skills as 
measured by impaired grapheme-to-phoneme conversion. Second, because 
Montgomery's (1981) version of the articulatory awareness test was unavailable, the 



29 

present study used an alternative but similar version of the test, which was based on 

Montgomery's task. 

Is There Support for the Articulator/ Feedback Hypothesis of Naming? 

Prediction for individuals with adequate articulatorv knowledge . The articulatory 
feedback hypothesis of naming stated that having articulatory feedback appropriate to 
naming facilitates name retrieval. This study tested this via an interference experimental 
design. If having articulatory feedback appropriate to naming facilitates name retrieval, 
interfering with that appropriate articulatory feedback should reduce facilitation effects. 

Prediction for individuals with impaired articulatorv knowledge . The hypothesis 
implied that those with poor articulatory knowledge will retrieve names less efficiently 
than those with good articulatory knowledge. In an interference paradigm, where 
subjects were asked to engage in another task that produced articulatory feedback 
incompatible with the naming task at hand, those with poor articulatory knowledge were 
predicted to perform differently than controls. Whereas the controls' naming should be 
de-facilitated, those with poor articulatory knowledge may respond in one of two ways. 
One, because they may not rely in the articulatory feedback system to facilitate name 
retrieval in the first place, interfering with articulatory feedback may not produce de- 
facilitation effects as expected with controls. Or possibly, because their articulatory 
feedback system was already poor, adding another task with demands on the articulatory 
feedback system may exacerbate the difficulty these subjects experience, leading to even 
worse naming performance than controls' de-facilitated naming performance. 



30 

Group differences. If Montgomery's (1981) finding is supported and those with 

phonological impairments have worse articulatory knowledge compared to controls, then 
performance of phonological ly impaired subjects can be compared to the performance of 
controls. Even if Montgomery's finding is not supported, there is theoretical interest in 
comparing the articulatory knowledge of these two groups as it has been well 
documented that dyslexic individuals have name retrieval difficulties. 

Those with phonological impairment can be further divided into two subgroups: 
those with impaired reading skills and those with adequate reading skills. Performance of 
these two subgroups can be compared to examine if these subtypes show different 
patterns on articulatory knowledge and name retrieval, or if they differ only in the degree 
of severity. 

To test the hypothesis most directly, subjects can be grouped according to their 
performance on a measure of articulatory knowledge. These three ways of grouping 
subjects (i.e., phonologically impaired vs. controls; phonologically and reading impaired 
vs. phonologically impaired with adequate reading vs. controls; poor articulatory 
knowledge vs. adequate articulatory knowledge) may yield performance patterns that 
further elucidate the relationship between articulatory knowledge and name retrieval. 

What Is the Relationship Between Artirnlatorv Knowledge .n^ PhonoloPic.l A w.r.n.e.9 

Another secondary aim of this study was to elucidate the relationship between 
articulatory knowledge and phonological awareness. Much about phonological 
awareness among the dyslexic population has been studied, but little is known about 
articulatory knowledge. Are they related or independent of one another? What are the 



31 

factors that relate to or predict the level of articulatory knowledge and phonological 



awareness? 






METHODS 

Subjects 

Three groups of subjects totaling 41 children were recruited from the Gainesville, 
Florida and Chicago, Illinois metropolitan areas. Subjects were recruited from offices of 
psychologists, speech pathologists, and neurologists, and from fiyers distributed 
throughout the community. All subjects' parents gave written informed consent and all 
subjects gave oral assent to participate in this study in accordance with the requirements 
of the Institutional Review Board of the Health Science Center of the University of 
Florida and of the University of Chicago Hospitals. 

Inclusionary criteria for subjects included: 

• Age 7-12 

• Right-handed 

• Intelligence quotient between 70 and 1 30 

• English is first and primary language 

A lower limit of 7 years of age was selected because reading disability is often not 
apparent until school age; a large percentage of children have age-appropriate, limited 
reading skills before that time. An upper age limit of 12 was selected because beyond 
this age, children with developmental phonological dyslexia have had several years of 
struggling with reading. They may have received special services or developed other 
skills on their own in order to compensate for their impaired reading. While older 

32 



33 
phonological dyslexic children may still demonstrate naming problems, their retrieval 

deficit is often mitigated b\' late adolescence (Korhonen, 1995; Fawcett & Nicolson, 
1994; Felton et al., 1990). Forms of compensation may confound the contribution of 
articulatory feedback to name retneval. The age range was limited between 7-0 and 12- 
1 1 in order to include indix iduals in the early years of developmental phonological 
dyslexia. Right-handedness was selected as a predictor of typical language organization 
so that results from subjects in this study can be generalized to the population. 
Approximately 98% of nght-handed individuals are left hemisphere dominant for 
language. The intelligence critenon was constrained to the middle 96% of the 
population. Individuals at the extremes of the continuum may not process linguistic 
information in the same way as most individuals. The intelligence criterion was set so 
that results can be generalized to the population. English was required as subjects' first 
and primary language in order to rule out reading problems due to socio-cultural or 
environmental factors. 

Exclusionary criteria included: 

• History of neurological disorders 

• Previous treatment in Lindamood or Orton-Gillingham programs 

• Family history of learning disability 

A history of neurological disorders, such as cerebral palsy, epilepsy, or Tourette's 
Syndrome, increases the probability of atypical brain organization. Therefore individuals 
with neurological histones were excluded. Individuals who have participated in reading 
treatmem programs described as or based on the Lindamood or Orton-Gillingham 
programs were also excluded. These treatment programs include direct or indirect 



34 
training of articulatory awareness via training of articulatory gestures. A history of 

participation in these programs may confound results because subjects' articulatory 

feedback system may no longer reflect its naturalistic connectivity. Family members of 

individuals with a learning disability were also excluded because of anatomical studies 

suggesting a genetic basis to learning disability (Leonard et al., 1993). 

Subjects meeting criteria for the following three groups were recruited: 

• Developmental phonological dyslexia (DPD) 

• Adequate reader with poor phonology (ARPP) 

• Normal reader controls (CTRL) 

Group membership was distinguished by performance on three reading 
achievement subtests of the Woodcock Reading Mastery Test (WRMT; Woodcock, 
1987): Word Identification, Passage Comprehension, and Word Attack. The Word 
Identification subtest consisted of single English words that subjects were asked to read. 
This subtest assessed subjects' oral reading of real words, but no comprehension of word 
meaning was required. The Passage Comprehension subtest consisted of a short sentence 
or paragraph xvith one missing word. Subjects were required to read the entire passage 
and come up vvith one word that would fill the missing blank. This subtest assessed 
subjects' comprehension of wntten material. The Word Attack subtest consisted of 
nonwords that followed the rules of pronunciation in the English language. Subjects 
were required to read these words aloud. This subtest required subjects to use the 
grapheme-to-phoneme conversion route to read. Raw scores were converted to age- 
corrected standard scores for each of these three measures. 



35 



Subjects in the DPD group had impaired reading achievement scores on all three 
subtests in comparison to that expected given their intellectual aptitude, as assessed with 
the Test of Nonverbal Intelligence, 2"^^ Edition (TONI-2; Brown, Sherbenou, & Johnsen, 
1990). Impairment was operational ized as actual achievement score falling at least one 
standard deviation (i.e., 1 5 standard score points) below the expected score, with the 
expected score calculated using the formula 

Y'= [r,v(S,/Sy)(IQ-X)] + Y 
(Y- = expected achievement score, r^ = estimated correlation between the TONI-2 and 
the WRMT, S, = standard deviation of WRMT [15], S, = standard deviation of TONI-2 
[15], IQ = obtained TONI-2 Quotient, X = mean of TONI-2 [100], and Y = mean of 
WRMT [100]). 

Subjects in the ARPP group had impaired phonological skills, operational ized by 
impaired actual Word Attack score in comparison to the expected score based on TONI-2 
Quotient, but non-impaired reading skills as defined by commensurate actual and 
expected Word Identification and Passage Comprehension scores. These subjects, as 
subjects in the DPD group, were recruited as poor readers. The categorization into DPD 
or ARPP groups was done after each subject's completion of participation. 

Subjects in the CTRL group were matched to the other two groups on age and 
intelligence. The CTRL group's expected reading achievement scores based on 
intellectual aptitude were all commensurate with actual achievement scores. The CTRL 
group was not matched to the other two groups on reading age because the pnmary 
purpose of this study was to evaluate name retrieval ability. The ability to retrieve names 



36 
may be affected by age and intelligence. Thus all three groups were matched on age and 

intelligence. Table 1 summarized the grouping criteria. 

Table 1 Summary of grouping criteria. 

Group Word Attack Word Identification Passage Comprehension 



DPD ASS < ESS ASS~<~ESS ASS'^sY 

ARPP ASS < ESS ASS = ESS ASS = ESS 

CTRL ASS = ESS ASS = ESS ASS = ESS 



Note: DPD - Developmental Phonological Dyslexia; ARPP = Adequate Reader with 
Poor Phonology; CTRL = Controls; ASS = actual standard score; ESS = expected 
standard score based on the formula, Y' = [r^ (S, / Sv )(IQ - X)] + Y" lO = TONI-2 
Quotient. ' j ^ v< 



i*; V 



The TONI-2 was selected as the measure of intellectual ability for this study 
because of its relative lack of dependence on verbal abilities. A problem with measuring 
dyslexic children's general intelligence is that most common measures of intelligence 
rely heavily on verbal abilities. Because dyslexic children have impaired reading skills, 
they may obtain intelligence scores that are lower than their "true" intellectual capability. 
To minimize this problem, intelligence quotient of the TONI-2 was used as the measure 
of intelligence for this study. The TONI-2 stimuli consisted of visual patterns with a 
missing piece. Subjects were required to choose a pattern to fit the visual sequence from 
multiple choices. While good performance on this test may still benefit from 
verbalization, the TONI-2 has less verbal demands than most other measures of 
intelligence. The TONI-2 can be used with subjects from age 5 to 85. Nonnative data 
was collected from over 2,700 subjects in these age ranges. The TONI-2 Form B's 
correlation with the WISC-R FSIQ ranged from .75 to .94. Its correlation with WISC-R 



37 

VIQ ranged from .63 to .73, and its correlation with WISC-R PIQ ranged from .60 to .87. 

Form B was selected to be used in this study because of its relatively more stable 
correlation with WISC-R indices compared to Form A. Raw scores on the TONI-2 were 
converted to age-corrected TONI-2 Quotients, which have a mean of 100 and a standard 
deviation of 15. 

Table 2 summanzed each group's demographic data and grouping cnteria scores. 
All three groups were matched on age (F = 0.35, p = 0.71), intelligence score (F = \.50,p 
= 0.24), and grade (F = 0.74, p = 0.48). More males were represented in the DPD group 
in comparison to the other two groups. The three groups did differ from each other on 
the three reading achievement subtests (F = 7.30, p = 0.00). For each of the three reading 
achievement subtests, the DPD group scored uniformly lower than the other groups 
(Word Attack: DPD vs. ARPP, t = 2.67, p = 0.02, DPD vs. CTRL, t = 9.65, p = 0.00; 
Word Identification; DPD vs. ARPP, t = 4.86, y? = 0.00, DPD vs. CTRL, t = 9.36,/? = 
0.00; Passage Comprehension: DPD vs. ARPP, t = 6.48,/? = 0.00, DPD vs. CTRL, t = 
S.\0,p = 0.00). The ARPP group scored lower than the CTRL group on Word Attack (t 
= 7.\4,p = 0.00) and Word Identification (t = 4.60, p = 0.00), but not on Passage 
Comprehension (t = \.99,p = 0.06). Within the DPD and CTRL groups, there was no 
difference between any of the three achievement scores (DPD, F = 2.82,/? = 0. 1 1 CTRL 
F = \.24,p = 0.32). The ARPP group, however, demonstrated better Passage 
Comprehension compared to Word Identification (t = 7.40, p = 0.00), which in turn was 
better than Word Attack (t = -8.71 , p = 0.00). 



Table 2. Summary of demographics and grouping criteria scores. 



38 





DPD 


ARPP 


CTRL 


N 


11 


10 


20 


Age 


9.0(1.1) 


9.4(1.9) 


9.5(1.7) 


Grade 


3(1) 


4(2) 


4(2) 


M;F Ratio 


9:2 


6:4 


12:8 


ADHD M:F Ratio 


5:1 


3:0 


3:0 


TONI-2 IQ 


103(13) 


110(7) 


106 (8) 


Word Attack SS 


70(11)" 


81 (8)^ 


104 (8)" 


Word Identification SS 


68(12)" 


89 (7)*^ 


105(10)" 


Passage Comprehension SS 


70(12)" 


96 (5)^ 


103 (10)'= 



Note: DPD - Developmental Phonological Dyslexia; ARPP = Adequate Reader with 
Poor Phonology; CTRL = Controls. Scores with different superscnpts indicate 
statistically significant difference. 



Descriptive Measures 

Subjects were administered a battery of relevant tests to compare performance 
between groups and for comparison with other findings in the literature. Data from the 
following descriptive measures were used to describe the groups on relevant 
neuropsychological variables. 



Articulato rv Awareness Test (A AT) 

The Articulatory Awareness Test (AAT) is a non-published, experimental 
instrument modeled after Montgomery's (1981 ) task. The AAT stimuli consisted of eight 
picture cards with cartoon drawings of the sagittal view of the oral cavity (see Appendix 



39 

1 ). Each picture represented one or more phoneme. The examiner produced a target 

phoneme with her mouth obstructed from subject's view, then asked the subject to 
identify one of out three pictures that corresponds to subject's articulatory gesture as s/he 
produced that phoneme. Subjects were encouraged to repeat the target phoneme as many 
times as necessary, and no time limit to responding was imposed. Three practice items 
were given before test items were administered, and the examiner went over a sagittal 
cartoon drawing to identify each articulator (i.e., tongue, teeth, lips) at the introduction of 
the task. In the event that subject produced an atypical articulator)' gesture in producing a 
phoneme, the subject's gesture was used in scoring accuracy. 

The AAT was produced by the Morris Center of Gainesville, Florida and used as 
part of their standard evaluation for reading disability. The AAT consisted of 10 trials, 
with 10 additional trials added and piloted during this study (AAT-R). Data on the 
original 10 tnals were available from 93 patients from the Morris Center. Seventy 
percent of these subjects were male (n = 65). Thirty percent were female (n = 28). 
Patients' ages ranged from 6 to 22, with an average of 1 1 years (standard deviation = 4). 
Of these 93 subjects, 86 were classified as dyslexic, 7 were classified as borderline 
dyslexic; 81 of these subjects were diagnosed with co-morbid ADHD. Table 3 shows 
group means on the AAT (out of possible 10 tnals). Dyslexic subjects performed more 
poorly on the AAT than borderiine dyslexics (t = 2.03, p- 0.04). AAT scores did not 
differ between ADHD and non-ADHD groups (t = 1 .34, /, = 0. 1 8). Because the Morris 
Center population did not include normal readers, it was not known whether including 
normal readers would yield a bimodal distribution of AAT scores, as Montgomery (1981) 
showed with her version of this task. The data available from the Moms Center 



40 

suggested a normal distribution of scores on the AAT from the dyslexic population. This 

test was administered to all subjects in the present study to estimate subjects' level of 
knowledge about articulator position during phoneme production. The number of 
accurate responses in 10 trials (AAT) and in 20 trials (AAT-R) was recorded for analysis. 





n 


Sl-r. 

Mean AAT score (SD) 


Dyslexic 


86 


7 (2) „ 


Borderline Dyslexic 


7 


8(1) ' 


ADHD 


81 


7(2) 


Non-ADHD 


12 


8(1) 



Total 



93 



7(2) 



Naming 



Subjects were administered the Boston Nammg Test (BNT; Kaplan, Goodglass, 
Wemtraub, & Segal, 1983) as a measure of visual confrontation naming. Z-scores 
calculated from the norms published by Spreen and Strauss (1998) were recorded for 
analysis. Subjects were also administered the Rapid Color Naming and Rapid Object 
Naming subtests of the 1997 experimental version of the Comprehensive Test of 
Phonological Processing, which was same as the 1999 published version of the same test 
(Wagner, Torgesen, & Rashotte, 1999). Z-scores based on normative data collected in 
1997 by the research group developing this battery were calculated and recorded for 



analysis. These measures assessed subjects' rapid naming ability and allowed for 
comparison of data from the present subject groups to other findings reported in the 



41 



literature. 



Phonological Awareness 



The Lindamood Auditory Conceptualization Test (LAC; Lindamood & 
Lindamood, 1979) was administered to all subjects to yield an index of phonological 
awareness. This test assessed subjects' phonological awareness by asking subjects to 
manipulate color blocks, with each color representing one phoneme. Phoneme patterns 
changed in degrees of difficulty, and subjects manipulated blocks to demonstrate their 
perception of how phoneme patterns changed. Raw scores from the LAC were recorded 
for analysis. 

At tention-DeficitHyp e ractivitvDisnrdpr 

Subjects' parents were interviewed using a semi-structured interview for 
symptoms of ADHD based on DSM-IV critena. This questionnaire asked about each 
symptom listed in the DSM-IV, and if the parent endorsed six or more symptoms of 
either the inattention and/or hyperactivity/impulsivity cluster, follow-up questions about 
age of onset, duration of symptoms, situations where symptoms are exhibited, and extent 
of symptoms' disturbance on functioning were asked. Based on parents' response to this 
questionnaire, subjects were categonzed into ADHD or Non-ADHD groups. Because of 
the high co-morbidity rate between dyslexia and ADHD, this interview allowed for the 



42 

description of ADHD rate in the current study groups. The form used during this 

interview is presented in Appendix 2. 

Experimental Measures 

The aim of this study was to test the hypothesis that articulatory feedback 
facihtates naming. According to this hypothesis, feedback from appropriate articulator 
movements facilitates name retrieval. An interference paradigm was implemented to test 
this hypothesis. While subjects attempted to name objects, they were asked to engage in 
another task designed to interfere with articulatory movements appropriate to the naming 
task. 

Naming Assessed via Phoneme Match (NAPM^ 

During the experimental task, NAPM, subjects were required to look at two 
pictures, name those pictures to themselves, and determine if those names end in the 
same phoneme. They engaged in two interference conditions while performing this 
naming task. During the Mouth Interference condition, subjects were asked to engage 
their mouth in the following movement sequence: Lips together (as if making the /m/ ■ 
sound)-tongue between teeth (as if making the /th/ sound). These movements were 
demonstrated without accompanying phonemic sounds. Because subjects' articulators 
were engaged in this interference movement, they could not orally name the objects seen. 
Therefore naming was assessed by asking subjects to decide if the names of the two 
objects seen during each trial terminated in the same sound. They indicated their 



' ■- " ' 43 



response by pressing designated buttons. In order to perform this task, subjects must first 
name the two objects, then judge if those names have matching end phonemes. 

Dunng the Foot Interference condition, subjects were asked to move their left foot 
in a rocking movement alternating between heel and toe, while naming pictures and 
deciding if the names' end phoneme matched. This condition was implemented to control 
for the attentional demands of engaging in an interference task. Subjects engaged in 
Mouth Interference while performing the NAPM task during half of the trials and 
engaged in Foot Interference during the other half of the trials. The order of the 
interference condition was counterbalanced across subjects. 

Each interference condition consisted of 32 trials. Half of the trials had word 
pairs with matching end phonemes and the other half had word pairs with non-matching 
end phonemes. The 64 word pairs were divided into two stimulus sets and are presented 
m Appendix 3. The two stimulus sets were balanced on word frequency (Francis & 
Kucera, 1982), number of syllables, and grade level by which the word is taught 
(Thomdike & Urge, 1972). Simple black and white line drawmgs eliciting each target 
were drawn from the Snodgrass and Vanderwart picture set (1980), Peabody Picture 
Vocabulary Test (Dunn & Dunn, 1981), and Boston Naming Test (Kaplan et al., 1983). 
In some instances, a lack of available drawings necessitated the use of locally produced 
drawings, which were produced to be of similar visual complexity level as pictures from 
above memioned sets. Each stimulus set was used during Mouth Interference half the 
time and during Foot Interference half the time. 

These stimuli were presented to subjects on a laptop computer via a program 
written with PsychLab v.6.0.2. Each tnal began with a fixation mark lasting 1000 msec. 



44 
Then an auditorv' cue alerted subjects to the onset of pictured stimuli, which remained on 

the screen until subjects pressed one of two acceptable keys. The computer recorded 
subjects' response and reaction time from the onset of the stimuli presentation to key 
press. The examiner monitored subjects' interference movement and recorded the 
number of movement cycles completed during each trial. One movement cycles during 
the Mouth Interference was defined as lip closure followed by intrusion of the tongue 
between teeth. One cycle during the Foot Interference was defined as toe touching the 
floor followed by heel touching the floor. A different auditory cue followed subjects' 
response and marked the end of a trial. The screen then remained blank until the 
examiner pressed one of two keys marking that tnal as valid or invalid. No time limit on 
response time was imposed. Because the next trial did not begin until the examiner 
pressed one of two keys, the examiner controlled the pace of the testing and implemented 
breaks as appropriate for each subject. 

The NAPM began with six practice trials, during which subjects performed the 
NAPM task without any interference. Each interference condition (Mouth and Foot 
Interference) began with a demonstration of the interference task, followed by four 
practice trials with interference. Subjects were instructed to engage in the interference 
movement before the onset of each trial. After the practice trials, subjects were informed 
that testing will begin. The first two trials were used as buffer trials (i.e., data were not 
recorded) without subjects' knowledge. The 32 experimental trials followed. Data 
recorded during each trial included response reaction time, response (to calculate 
accuracy percentage), and number of interfering movements produced (for calculating 



45 

average frequency of movement). Time taken to complete the NAPM ranged between 15 

to 20 minutes. 
Visual Match 

A visual match task was implemented to control for potentially different 
attentions demands of Mouth and Foot Interference. This was a nonverbal, visual match 
task requiring subjects to determine if one of four pictures matched a target. Subjects 
engage in Mouth and Foot Interference during this task as well. If subjects' performance 
during the Mouth and Foot Interference conditions differed on this non-verbal task, that 
would suggest that mouth movements and foot movements have differem levels of 
interfering effect. ,v 

Similar to the NAPM, Visual Match also had 32 trials for each interference 
condition. Each trial composed of one target picture at the top of the screen and four 
other pictures at the bottom of the screen. Subjects were instructed to press one of two 
keys indicating whether there was a match between the four pictures on the bottom and 
the target on top. Half of the tnals had matching pictures and the other half had non- 
matching pictures. Pictures were taken from the Test of Visual-Perceptual Skills (non- 
motor)~Revised (Gardner, 1996), and were selected for their difficulty to be verbalized. 
One set of stimuli was constructed first. The second set was constructed by changing the 
target and/or the ordenng of the four pictures on the bottom. 

The Visual Match Test was presented to subjects on a laptop computer via a 
program wntten with PsychLab v.6.0.2. Experimental parameters mirrored the NAPM 
parameters as much as possible. Each tnal began with a fixation mark lasting 1000 msec. 



46 

Then an auditory cue alerted subjects to the onset of pictured stimuli, which remained on 

the screen until subjects pressed one of two acceptable keys. No time limit to response 
was imposed. The computer recorded subjects' response and reaction time. The 
examiner recorded the number of interference movement cycles completed during each 
trial. A different auditory cue followed subjects' response and marked the end of a trial. 
The screen then remained blank until the examiner pressed one of two keys marking that 
trial as valid or invalid. 'i'l'.lM " 

Similar to the NAPM, the Visual Match Test also began with six practice trials, 
during which subjects performed the Visual Match task without any interference. 
Interference conditions (Mouth and Foot Interference) then followed. Each condition 
began with a demonstration of the interference task, followed by four practice tnals with 
interference. Subjects were instructed to engage in interference movemem before the 
onset of each tnal. After the practice tnals, subjects were informed that testing will 
begin. The first two trials were used as buffer tnals (i.e., data were not recorded) without 
subjects' knowledge. Thirty-two expenmental tnals followed. The order of the 
interference conditions was counterbalanced across subjects. Data recorded dunng each 
trail included response reaction time, response accuracy, and the number of interfenng 
movements produced (for calculating average frequency of movement). Time taken to 
complete the Visual Match Test ranged between 1 5 to 25 minutes. 

Phoneme Matrh 

A Phoneme Match Test was implemented to control for the potential difference in 
subjects' ability to detemiine if the end phoneme of word pairs matched. This was a 



47 

necessary control given that subjects' naming performance dunng the NAPM was 

measured via their abihty to determine matching phonemes. Subjects completed this task 
without any interference. Stimuli were two sets of word pairs used during the NAPM. 
These stimuli were presented aurally to subjects on a laptop computer via a program 
written with PsychLab v.6.0.2. Each tnal began with the word "listen," which stayed on 
the screen until the end of the trial. A pair of words was presented by the computer 1500 
msec after the onset of the word "listen."' Subjects pressed one of two keys to indicate if 
the two words' last phonemes matched. The computer recorded subjects' response and 
reaction time from the onset of stimulus presentation. The screen then remained blank 
until the examiner pressed one of two keys marking that tnal as valid or invalid. No time 
limit on response time was imposed. 

The Phoneme Match Test included four practice trials followed by the two 
stimulus sets, which totaled 64 trials. The order of the two stimulus sets was 
counterbalanced across subjects. Data recorded during each trial included response 
reaction time and response accuracy. Time taken to complete the Phoneme Match Test 
ranged between 5 to 1 minutes. 

Naming Test 



> ■ > & -i ^^^ -■■' 



A visual confrontation naming test was administered after the completion of the 
NAPM, Visual Match, and the Phoneme Match Test. This Naming Test composed of 
black and white line drawings from the NAPM. Each subject was asked to say the 
that they assigned the pictured item when they saw it during the NAPM task. For cases 
where subjects stated an acceptable alternative response for an item (e.g., "bunny" for 



name 



t 



' 48 

"rabbit"), the name that the subject gave was used to determine if the two items from that 



I . ': 



NAPM trial had names with matching end phonemes, and the subject's response 
accuracy for that trial was determined accordingly. For cases where subjects were not 
able to produce a response because of unfamiliarity with the object, the NAPM trial 
including that object was deleted. Thus response to objects for which subjects were 
unfamiliar was not included in data analysis. 

Procedures 

The examiner first interviewed the parent over the telephone to obtain each 
subject's background information and to screen for ADHD. Based on this information, 
subjects were assigned a subject number using the chart represented in Table 4. This 
chart counterbalanced the order of task presentation (i.e., NAPM or Visual Match), the 
order of stimulus sets used, and the order of interference conditions. Each subject's order 
of test presentation, stimulus set used, and order of interference condition was determined 
based on his/her assigned subject number. 

Subjects completed the testing in either one or two settings, totaling 1 .5 hour for 
older normal readers to 3 hours for younger subjects with reading problems. Factors 
influencing whether testing was completed in one or two settings included each subject's 
time availability and their performance during the first hour. For those subjects who 
expenenced difficulty, testing was completed in two sessions to minimize their 
frustration. Subjects were encouraged and praised for their effort rather than for their 
accuracy. In no instance were subjects given feedback about the accuracy of their 



49 



Table 4. Sample of the chart for determining the order of task, interference, and stimulus 
set for each subject. 





Controls 




Phonologically 


Impaired 




Order of 


NAPM 


Visual Match 


NAPM 


Visual Match 




stimulus set 


first 


first 


first 


first 




AB 


1001 


: 1002 


2001 


2002 


Mouth 


BA 


1003 


1004 


2003 


2004 


Interference 


AB 


1005 


1006 


2005 


2006 


first 


BA 


1007 


1008 


2007 


2008 




AB 


1009 


1010 


2009 


2010 




BA 


1011 


1012 


2011 


2012 


Foot 


AB 


1013 


1014 


2013 


2014 


Interference 


BA 


1015 


1016 


2015 


2016 


first 


AB 


1017 


1018 


2017 


2018 




BA 


1019 


1020 


2019 


2020 





response. The order in which testing components were completed is represented in Table 



The Test of Phonological Awareness (Torgesen & Bryant, 1994) was used to train 
subjects to match end phonemes of words, thus familianzmg them to the task demand of 
the NAPM. Subjects' performance on this task was not scored. The examiner remained 
with subjects throughout testing. During the NAPM and Visual Match tasks, the 
examiner monitored subjects' interference movements (mouth or foot) and reminded 



50 

Table 5. Order of test administration. 

Telephone interview: 

Demographic Questionnaire 
ADHD Questionnaire 
Testing session: 

Test of Phonological Awareness 
NAPM and Visual Match 

Mouth and Foot Interference 
Phoneme Match Test ,- • 

Naming Test 
Boston Naming Test 
Articulatory Awareness Test 
Lindamood Auditory Conceptualization Test 
Woodcock Reading Mastery Test: 

Word Identification 

Word Attack 

Passage Comprehension 
Rapid Color Naming and Rapid Object Naming 



subjects to engage in these movements during instances when they stopped. These parts 
of the session were videotaped. 



.•A 



V RESULTS 

The statistical program, SPSS v7.5.2 for Windows, was used to analyze the 
following data. Both means and medians were examined as central tendency statistics for 
reaction time data (i.e., NAPM, Visual Match, Phoneme Match). Because reaction times 
are subject to floor effects and have unlimited ceilings, distribution of scores may be 
skewed and means may be overly influenced by extremely slow reaction times. Thus 
extreme scores were trimmed in the following way in calculating means for each subject. 
For each subject's performance in each condition (i.e., NAPM Mouth Interference, 
NAPM Foot Interference, Visual Match Mouth Interference, Visual Match Foot 
Interference, Phoneme Match Set A, Phoneme Match Set B), mean reaction time and 
standard deviation were calculated. Extreme scores that lay outside of two standard 
deviations from the mean were excluded, yielding a trimmed mean for each condition for 
each subject. There was no difference in the pattern of results using trimmed means and 
medians. Thus results reported here were based on trimmed means. 

Articulatorv Knowledge 

The Articulatory Feedback Hypothesis of Naming stated that articulatory 
feedback facilitates naming. This hypothesis implied that better articulatory knowledge 
should be correlated with faster reaction time on a naming test. This was tested using a 
regression analysis with performance on the Articulatory Awareness Test as the 



51 



52 

independent variable and reaction time on experimental measures as dependent variable. 

The experimental measures considered here included the NAPM and Visual Match, 
Mouth and Foot Interference conditions. The Visual Match was included as a non-verbal 
control task. The hypothesis predicted a significant correlation between Articulatory 
Awareness Test scores and NAPM, a name retrieval task, but not with Visual Match, a 
nonverbal control task. The two interference conditions of the NAPM were predicted to 
have different correlations with articulatory knowledge because subjects were able to use 
appropriate articulatory feedback in one condition (i.e., Foot Interference) but not in the 
other (i.e.. Mouth Interference). In the condition where subjects were able to use 
appropriate articulatory feedback (i.e.. Foot Interference), better articulatory knowledge 
was expected to be associated with faster naming time. In the condition where subjects 
were not able to use articulatory feedback (i.e.. Mouth Interference), reaction time was 
expected to be slow; thus a non-significant correlation between articulatory knowledge 
and naming time may be seen. 

The Pearson correlations between reaction time on experimental measures and 
scores on the Articulatory Awareness Test (10-item version) and the Articulatory 
Awareness Test-Revised (AAT-R, 20-item version) were reported in Table 6. Reaction 
time during the two interference conditions of the NAPM were either significantly 
correlated with or approaching significance with the AAT (Mouth F = 4.52,/? = .04; Foot 
F = 3.48,/? = .07) and the AAT-R scores (Mouth F = 5.64,/? = .02; Foot F = 3.74, p = 
.06), whereas there was no relationship between reaction time on the Visual Match Test 
and articulatory knowledge (AAT: Mouth F = 0.21,/? = .65; Foot F = 0.92,/? = .34; AAT- 
R: Mouth F = 0.23, p = .64; Foot F = 0.37, p = .55). This pattern indicated that 



53 

increasing articulatory knowledge was associated with faster reaction time during name 

retrieval but not during a nonverbal visual match task. However, the prediction that 
articulatory knowledge would be more highly correlated with naming latency during the 
Foot Interference than during the Mouth Interference condition was not supported. 



Table 6. Pearson correlations betw een the Articulatory Awareness Test (AAT) score and 
reaction time on experimental measures. 





AAT 


AAT-R 


NAPM 






Mouth Interference 


-.32* 


-.36* 


Foot Interference 


-.29" 


-.30" 


Visual Match 






Mouth Interference 


.07 


.08 


Foot Interference 


.15 


.10 



Note: p<.05, p<.lO. 

Phonologically Impaired vs. Controls 

Montgomery (1981) found a difference in articulator^- knowledge between 
dyslexic children and normal readers. To examine if that finding can be replicated 
among the present subjects, the difference in articulator)- knowledge between normal 
readers and phonologically impaired readers was examined. The Phonologically 
Impaired (PI) group was composed of the DPD and ARPP subjects. 



«. « -<C ^t .. 



54 

Articulatorv Awareness Test 

Scores obtained by PI and CTRL groups were reported in Table 7. A t-test of 
independent samples was utilized to test for significant differences between the two 
groups. Unlike Montgomery's (1981 ) finding, there was no difference beUveen PI and 
CTRL groups on articulatory knowledge as assessed by the 10-item version of the AAT (t 
= 0.80, p = .43) or by the 20-item version (AAT-R, t = 0.64, p = .53). 

Table 7. AAT and AAT-R scores obtained by Phonologically Impaired (PI) and Control 
(CTRL) groups. 



PI CTRL 



AAT 6.19(2.27) 6.70(1.75) 

AAT-R 12.14(3.55) 12.85(3.53) 



Although no difference was found between groups on articulatory knowledge 
measures, it was possible that subjects with phonological impairment may demonstrate a 
different pattern of relationship between articulatory knowledge and name retrieval 
compared to controls. Thus Pearson correlations between the AAT score and reaction 
time during each experimental condition (i.e., NAPM and Visual Match Mouth and Foot 
Interference conditions) were examined for each group. Because the AAT and AAT-R 
scores yielded similar pattern of results, only AAT scores were reported. 

Table 8 showed Pearson correlations between the AAT score and reaction time 
during experimental conditions for each group. The correlation between the articulatory 
knowledge score and naming latency (i.e., NAPM) seen in the analysis with all subjects 
combined was driven by the control subjects (Mouth, F = 5.31,/? = .03; Foot, F = 2.53,/? 



55 

= .13). The speed by which PI subjects retrieved names was not related to their level of 

articulatory knowledge (Mouth, F = 0.65, p = .43; Foot, F = 0.84,;? = .37). Neither 
groups' AAT score was correlated with their performance on the visual match control. 
The control group's correlation between articulatory knowledge score and naming latency 
during Mouth Interference differed from their correlation between articulatory knowledge 
and visual match latency during Mouth Interference (-0.48 vs. 0.19; Z(2o,20) = -2.07, p< 
.05; subscripts denote the sample size of groups being compared). No other pairs of 
correlation differed from each other. ' 



Table 8. Pearson correlations between the AAT score and reaction time on experimental 
measures for PI and CTRL groups. 



PI CTRL 



NAPM 

Mouth Interference -. 1 8 -.48* 

Foot Interference -.21 -.35 
Visual Match 

Mouth Interference -.03 .19 

Foot Interference .06 .27 

Note: PI = Phonologically Impaired; CTRL = Controls; V < 05. 
Descriptive Measures 

Subjects' performance on descriptive measures were reported in Table 9. Scores 
were compared by t-test of independent samples. On measures of naming, the PI group 
performed more poody on the BNT (t = 2.05, p = .05), but not on Rapid Color Naming (t 
= 1.54, p = .13), Rapid Object Naming (t = \.47,p = . 15), or on the experimental Naming 



Test (t = 1.58,/7 = .12). The PI subjects scored significantly lower than the CTRL 
subjects on the LAC, our measure of phonological awareness (t = 3. 12,/? = .00). The 
TONI-2 and reading achievement scores were also reported to contrast between PI and 
CTRL groups. The PI did not differ from the CTRL group on intellectual aptitude as 
measured by the TONI-2 (t = -0. 14,/7 = .89), but their reading achievement scores were 
all worse than their age- and intelligence-matched controls (Word Attack, t = 9.28,/? = 
.00; Word Identification, t = 7.04, p = .00; Passage Comprehension, t = 4.87, /? = .00). 



56 



—— — ■ Si !___!_ 


PI 


CTRL 


BNT' 


-1.96(2.07)'= 


-0.85(1.30/ 


Rapid Color Naming^ 


-0.58(1.57) 


0.02 (0.68) 


Rapid Object Naming'' 


-0.88 (2.36) 


-0.05 (0.80) 


Naming Test"" 


87(6) 


90(6) 


LAC^ 


55(17)^ 


75 (23/ 


TONI-2'^ 


106(11) 


106(8) 


WRMT Word Attack'^ 


75(11)'= 


104(8/ 


WRMT Word Identification'* 


78(14)' 


105(10/ 


WRMT Passage Comprehension'* 

XT i . ¥-\T T^l .... 


83(16)'= 


103(10/ 



Note: PI - Phonologically Impaired; CTRL = Controls; ' Z-scores. ' Percentage correct 
Kaw score. Age-corrected standard scores. Within each row, numbers with different 
superscnpts were significantly different from each other. 



57 

Experimental Measures 

Phoneme match. Before group differences on the NAPM were further examined, 
subjects' abilitN to match phonemes was evaluated first. Name retneval on the NAPM 
was assessed via subjects' ability to match the end phoneme of words. Thus it was 
important to know if groups differed from each other on this ability. A multivariate 
analysis of variance (MANOVA) was performed on the reaction time data from the 
Phoneme Match Test with Group (PI vs. CTRL) as a between-subject variable and 
Stimulus Set (A and B) as a within-subject variable. This analysis also allowed for 
examination of differences between stimulus sets. Mean reaction time in milliseconds 
and standard deviations were presented in Table 10. The Group X Stimulus Set 
interaction was significant (F = 5.86, p = 0.02). Within each group, reaction times for the 
two stimulus sets were compared using dependent samples t-test. The CTRL group's 
reaction time on stimulus sets did not differ (t = 0.59, /7 = .56) while PI subjects were 
faster in responding to Set A than to Set B (t = -2.5 1 , p = .02). The group reaction time 
for each stimulus set was compared using independent samples t-test. The two groups' 
reactions times did not differ for Set A (t = -0.87, p = .39). The CTRL group was faster 
than the PI group on Set B (t = -2.08,;, = .04). A similar analysis was conducted on 
response accuracy. This revealed a marginally significant Group effect (F = 3.57,/; = 
.07). The CTRL group tended to be more accurate than the PI group. 

The two stimulus sets did not differ for the CTRL group, but for the PI group. Set 
B was responded to more slowly and therefore it may have been harder. The most 
important finding here was that the two groups did not differ in their ability to perform 
the Phoneme Match Test, as measured by both reaction time (F = 2.%5,p = .10) and 



58 

Table 10. Means and standard deviations of reaction time (RT in milliseconds) and 
accuracy (% Correct) on the Phoneme Match Test. 

H CTRL 



EI % Correct RT % Correct 

Stimulus Set A 2899(579)" 90(13) 2758(445) 95(5) 
Stimulus Set B 3224 (966)^ 89 ( 11) 271 1 (543)' 94 (7) 



Note; PI = Phonologically Impaired: CTRL = Controls. Numbers with different 
superscripts were significantly different from each other. 



accuracy (F = 3.57,/? = .07). Because there was no statistically significant group effect, 
and because the counterbalance measures taken to pair Set A with Mouth Interference 
approximately half of the time and with Foot Interference the other half of the time, the 
difference in difficulty level between Set A and Set B was considered to be equally 
dispersed among interference conditions. No further attempt to examine Stimulus Sets' 
interaction with other variables in subsequent analyses was taken because the number of 
subjects in this study limited the power available to detect such high level interactions. 

NAPM and visual match. Subjects' performance on the NAPM and Visual Match 
were reported in Table 11. Separate MANOVAs were conducted for reaction time and 
accuracy, with Group as a bet%veen-subject factor (PI vs. CTRL), and Task (NAPM and 
Visual Match) and Interference (Mouth and Foot) as within-subject factors. The 
MANOVA for reaction time revealed a Group X Task interaction (F = 6.61, p = .01) and 
a Task main effect (F = 27.23, p = .00). Subjects responded to NAPM faster than to 
Visual Match. Because reaction time to each task was not important theoretically, 
separate MANOVAs were conducted to further examine group differences and potential 
interference effects within each task. The MANOVA for NAPM revealed a marginal 



59 

Group effect (F = 3.91, /? = .06), with the CTRL group responding faster than the PI 

group. The MANOVA for Visual Match revealed no significant interactions or effects. 



Table 1 1 . Reaction time and accuracy on the NAPM and Visual Match Tests for PI and 
CTRL groups. 

PI CTRL 

EI % Correct RT % Correct 

NAPM ' ~" " ^ ■ ■ 

Mouth 4353(1547) 81(15) 3688(1551) 89(10) 

Foot 4591(1411) 83(8) 3653(1135) 89(10) 

Visual Match 

Mouth 4946(1788) 77(13)^ 5333(1694) 86(13) 

Foot 5004 0333^ Rd (Q\^ 



5004(1333) 84(9)'' 4984(1719) 84(13) 



Note: PI - Phonologically Impaired; CTRL = Controls. Numbers with different 
superscnpts were statistically different from each other. 

The MANOVA on accuracy data revealed a Group X Interference interaction (F = 
4. 1 1 , /. = .05) and a Group effect (F = 4.09, p = .05). Dependent samples t-test to 
compare the accuracy difference between interference conditions indicated that with the 
NAPM and Visual Match tasks combined, the CTRL group's accuracy on Mouth and 
Foot Interference conditions was the same (t = -0.72, p = .48), whereas the PI group 
achieved marginally less accuracy on Mouth Interference compared to Foot Interference 
(t = \.94,p = .07). Independent samples t-test to compare accuracy bet\veen groups 
showed that the PI group's accuracy on Mouth Interference was statistically less than the 
CTRL group's accuracy during this condition (t = 2.38, p = .02). Because there were a 
priori reasons^to examine the difference between interference conditions separately for 



60 

each task, and to examine if groups differed in this difference, dependent samples t-tests 

were conducted separately for the NAPM and the Visual Match Test. The only 
difference was found in the PI group's accuracy performance on the Visual Match Test. 
They were less accurate during the Mouth Interference condition than dunng the Foot 
Interference condition (t = 2.30, p = .03). Because their reaction time was not different 
between these conditions (t = 0.28,/? = .78), a speed-accuracy trade off was not a likely 
explanation for their worse accuracy during the Mouth Interference condition. 

Block effect . The present study included 12 individuals with ADHD. Children 
with ADHD may have decreased sustained attention span and/or slowed reaction time. 
Thus trials were divided into two blocks to examine if performance during the first half 
of each condition differed from performance dunng the second half A MANOVA on 
reaction time data with Group (PI vs. CTRL) as a between-subject factor and Task 
(NAPM and Visual Match), Interference (Mouth and Foot), and Block (1 and 2) as 
within-subject factors revealed a significant Task X Block interaction (F = 16. 15,/7 = .00) 
and Block effect (F = 5.06, p = .03), in addition to the Task X Group interaction and 
Task effect already reported above. Follow up MANOVAs were conducted for each 
Task. No significant effects were found for NAPM, but for Visual Match, a Block X 
Group interaction (F = 4.98, /7 = .03) was found as well as a Block effect (F = 20. 1 1,/7 = 
.00). Table 12 showed the reaction time on the NAPM and Visual Match tasks broken 
doxvn by Block. Dependent samples t-test indicated that the CTRL group's reaction time 
during Block 1 of Visual Match was much faster than their reaction time dunng Block 2 
(t = -4.49, p = .00), but such a difference was not found for the PI gfoup (t = -\.69,p = 
.11). 



61 

Table 12. Means and standard deviations of rea ction time for each block. 

PI CTRL 



NAPM 

Block 1 4582(1476) 3784(1462) 

Block 2 4374(1277) 3581(1206) 

Visual Match 

j»*V .11- 

Block! 4828(1451) 4768(1387)" 

Block2 5095(1606) 5564(1908)^ 



Note: PI = Phonologically Impaired: CTRL = Controls. Numbers with different 
superscripts were significantly different from each other. 

Similar analyses were conducted for response accuracy. The MANOVA revealed 
a significam Task X Interference X Block interaction (F = 5A6,p = .03), Task X Block 
interaction (F = 7.42, p = .0 1 ), and Block main effect (F = 4.0, p = .05), as well as an 
Interference X Group interaction and Group main effect. The latter two were discussed 
already in the section on accuracy and so were not discussed here. Of the three findings 
involving Block, only the highest level interaction was examined because lower level 
interactions were represented in the higher level interaction. Table 13 showed the 
response accuracy pattern reflected by the Task X Interference X Block interaction. 
Dependent samples t-tests were conducted to compare all possible pairs of scores. 
Subjects as one group became less accurate during the second block of Visual Match 
Mouth Interference (Block 1 vs. Block 2, t = 3.27, p = .00; Block 2, Mouth vs. Foot 
Interference, t == 3. 15,;. = .00). No other pair of scores was statistically different from 
each other. 



62 
Table 13. Response accuracy (percentage) reflecting the Task X Interference X Block 





Mouth 


Foot 


NAPM 






Block 1 


83 


86 


Block 2 


86 


85 


Visual Match 






Block 1 


85" 


85 


Block 2 


if 


83" 



Note: Numbers vvith different superscnpts were significantly different from each other. 



ore 



Separate MANOVAs were conducted for Block 1 and Block 2 to flirther expl 
how time influenced data. Table 14 summarized and compared the overall findings to 
Block 1 and Block 2 findings. The reaction time data were fairly consistent across the 
overall analysis and the two time blocks. The overall Group X Task interaction reflected 
faster response to the NAPM task by the CTRL group (see Table 1 1), but there was no 
difference between tasks m the PI group's reaction time. The increase in the Group X 
Task interaction from non-significance in Block 1 to significance in Block 2 could be 
understood by comparing Table 15 with Table 16. The CTRL group became faster on the 
NAPM with practice, but they slowed down significantly on the Visual Match with time. 
While such a pattern was also evident mth the PI group, their reaction time difference 
bet%veen Block 1 and Block 2 was not as dramatic. 

r Table 14 also shows differences in response accuracy findings between blocks. 
While the Group X Interference interaction was not significam in Block 2, the pattern of 



63 



Table 14. Comparison of overall findings with Block 1 and Block 2 findinfis. 

Overall Block 1 Block 2 



Reaction Time 














Group X Task 


6.67 


.01 


2.89 


.10 


8.86 


.00 


Task 


27.23 


.01 


8.05 


.01 


40.70 


.00 


Accuracy 












-^ 


Group X Interference 


4.11 


.05 


4.49 


.04 


2.93 


.10 


Group 


4.09 


.05 


4.75 


.04 


2.89 


.10 


Task X Interference 


.24 


.63 


.92 


.34 


5.71 


.02 


Task 


1.90 


.18 


.06 


.81 


5.31 


.03 



results in Block 2 was consistent with the pattern in Block 1 such that the overall Group 
X Interference interaction was significant. This interaction showed that the PI group was 
less accurate during Mouth Interference than Foot Interference, while the CTRL group's 
accuracy during both interference conditions was commensurate (see Table 11). The 
Task X Interference interaction significant in Block 2 was not significant m the overall 
analysis, suggesting a great degree of variability during Block 1. The Block 2 Task X 
Interference interaction reflected worse accuracy during the Visual Match Mouth 
Interference condition in comparison to the Visual Match Foot Interference condition and 
the two NAPM interference conditions. As can be seen in Table 16, this effect was 
mainly driven by the PI group's performance. 

The Block effects found were not anticipated a priori. Fatigue cannot completely 
explain differences between blocks because while reaction time slowed down for Visual 






64 
Table 15. Block 1 reaction time and accuracy on the NAPM and Visual Match Test for 
PI and CTRL groups. 

PI CTRL 

BI % Correct RT % Correct 



NAPM 

Mouth 4453(1757) 78(16) 3874(1944) 88(10) 

Foot 4710(1553) 84(9) 3695(1121) 88(12) 

Visual Match 

Mouth 4797(1651) 81(16) 4910(1454) 89(9) 

Foot 4859(1408) 85(11) 4625(1474) 85(14) 



Note: PI = Phonologically Impaired; CTRL = Controls. 

If'V^'-r^!''''^ ^ ''^^''^'''" ^™^ ^""^ accuracy on the NAPM and Visual Match Test for 
Pi and CTRL groups. 

PI CTRL 

EI % Correct RT % Correct 



NAPM 

Mouth 4264(1461) 83(17) 3543(1362) 89(12) 

Foot 4484(1366) 82(12) 3619(1292) 88(12) 

Visual Match 

Mouth 5035(1931) 72(16) 5768(2024) 82(19) 

Foot 5156(1407) 83(10) 5361(2027) 83(13) 



Note: PI = Phonologically Impaired; CTRL = Controls. ' T- 

Match, it speeded up for NAPM (Table 12). That accuracy was worse just dunng Visual 
Match Mouth Interference but not dunng Foot Interference also argued against an overall 



65 

fatigue effect (Table 13). Because the role of time was unclear, Block effects were also 

examined in subsequent analyses. 

Excluding ADHD subiects. To examine how ADHD subjects' reaction time 
differed from subjects without ADHD, their reaction time and accuracy data were 
contrasted from non-ADHD subjects in Table 17. Overall, subjects with ADHD tended 
to be slower and less accurate than their non-ADHD counterparts. Thus subjects with 
ADHD were excluded in a MANOVA to examine if ADHD subjects contnbuted to the 
findings involving Block. Similar to above. Group (PI vs. CTRL) was the between- 
subject factor, and Task (NAPM and Visual Match), Interference (Mouth and Foot), and 
Block (1 and 2) were within-subject factors. This analysis was similar to results reported 
with the ADHD subjects included. Significant interactions from reaction time data 
included Task X Block (F = 11 .66, /^ = .00) and Group X Block (F = 5.02, /? = .03). 
Significant main effects included Task (F = 20.47, p = .00) and Block (F = 7.32, /7 = .01 ). 
Dependem samples t-test to follow up the Task X Block interaction indicated that non- 
ADHD subjects were slower during Block 2 of Visual Match (t = A.36,p = .00), but 
their reaction times did not differ between NAPM Block 1 and Block 2 (t - 0.81,/7 = 
.42). Dependem samples t-test to follow up the Group X Block interaction indicated that 
the non-ADHD CTRL subjects were slower during Block 2 than Block 1 (t = -4. 10,;? = 
.00), but the non-ADHD PI subjects did not show a difference in reaction time between 
blocks (t = -0.28,/, = .78). Excluding ADHD subjects did not change theoretically 
important pattern of findings (i.e.. Group X Interference interaction). Repeating this 
MANOVA with accuracy data revealed no significant effects or interactions. Thus 
ADHD subjects were included in all subsequent analyses. 



Table 17. Reaction time and accuracy of the Non-ADHD and ADHD subgroups on the 
NAPM and Visual Match Tests. ^ 



66 





• 


PI 








CTRL 






■ ■■ 


Non- 




ADHD 




Non- 




ADHD 






ADHD 




(n = 9) 




ADHD 




(n = 3) 






(n=12) 








(n=17) 










RT 


% 
Correct 


RT 


% 
Correct 


RT 


% 
Correct 


RT 


% 
Correct 


NAPM 




















4109 


84 


4679 


76 


3432 


91 


5141 


75 


Mouth 


(1181) 


(12) 


(1964) 


(18) 


(1107) 


(7) 


(3055) 


(15) 


Foot 


4481 


86 


4739 


79 


3587 


91 


4028 


77 




(1692) 


(7) 


(999) 


(8) 


(1109) 


(8) 


(1467) 


(17) 



Visual 
Match 

5002 
Mouth (1532) 
Foot 4926 

(1225) 



82 



4871 



71 



5253 



(7) (2179) (17) (1733) 
82 5108 85 5020 

(7) (1536) (12) (1804) 



87 

(14) 

85 

(13) 



5786 
(1688) 

4780 
(1404) 



Note: PI = Phonologically Impaired; CTRL = Controls. 



81 

(8) 
78 

(12) 



Interference movements . The number of interference movements (mouth or foot) 
subjects completed dunng each tnal was recorded. The average number of movements 
completed per tnal withm each condition (i.e., NAPM Mouth and Foot Interference, 
Visual Match Mouth and Foot Interference) was div.ded by the average reaction t,me per 



trial for that condition to yield a movement frequency index (i.e., number of movements 
per second). Post hoc analyses were conducted with these data to determine if subjects 
engaged in mouth and foot interfenng movements with equal facility. A MANOVA with 
Group (PI vs. CTRL) as the between-subject factor and Task (NAPM and Visual Match), 
Interference (Mouth and Foot), and Block (1 and 2) as within-subject factors was 
conducted on the interfenng movement frequency data. This revealed a significant 
Group X Task X Interference X Block interaction (F = 4.35, p = .04), as well as 
. Interference (F = 4.5 1 , p == .04) and Block (F = 4.24, p = .05) main effects. Descriptive 
statistics were reported in Table 1 8. Perusal of these descriptive statistics revealed the 
following: There was no difference between PI and CTRL groups on Visual Match 
Mouth or Foot Interference, even when interference conditions were broken down by 
Block. There was also no difference between PI and CTRL groups on NAPM Foot 
Interference, even when broken down by Block. However, on NAPM Mouth 
Interference, the PI group produced interfenng mouth movements more slowly than the 
CTRL group. This difference was more salient during Block 1 than Block 2. 
To examine how interference movement frequency related to subjects' 
articulatory knowledge, Pearson correlations between the AAT score and interi-enng 
movement frequency indices were calculated and reported in Table 19. Significant 
correlations were found between the PI group's AAT score and their facility in producing 
interfenng mouth movements dunng NAPM. The better the PI group's articulatory 
knowledge, the faster they were able to produce interfenng mouth movements while 
engaging in a name retneval task. Unexpectedly, the PI group's AAT score was also 
con-elated with their facility in producing interfenng foot movements dunng the Visual 



68 

Table 18. Interfering movement frequency index (i.e., number of movements per second) 
for the PI and CTRL groups. 





PI 


CTRL 


NAPM 






Mouth 


.85 (.24) 


.97 (.32) 


Block 1 


.84 (.25) 


.98 (.35) 


Block 2 


.86 (.26) 


.95 (.31) 


Foot 


1.00 (.25) 


1.01 (.26) 


Block 1 


.98 (.25) 


.97 (.26) 


Block 2 


1.03 (.26) 


1.05 (.27) 


Visual Match 






Mouth 


1.00 (.23) 


.99 (.23) 


Block 1 


.99 (.24) 


.98 (.25) 


Block 2 


.99 (.22) 


1.01 (.24) 


Foot 


1.04 (.25) 


1.04 (.30) 


Block 1 


1.01 (.26) 


1.05 (.32) 


Block 2 


1.06 (.26) 


1.03 (.30) 



Note: PI = Phonologically Impaired; CTRL = Controls. 

Match Test. The better their articulatory knowledge, the more slowly PI subjects 
produced interfering foot movements during a nonverbal visual task. 

Predictors of Articulator^' Knowledge 

As a post hoc exploration of variables that predict performance on the AAT, age, 
BNT Z-score, Rapid Color Naming Z-score, Rapid Object Naming Z-score, LAC raw 



69 

Table 19. Pearson correlations between the AAT score and interfering mo\enient index 
for PI and CTRL groups. 



PI CTRL 



NAPM 

Mouth Interference .49* -.14 

Foot Interference -.03 -.24 
Visual Match 

Mouth Interference .02 -.14 

Foot Interference -.44* -.13 
Note: PI = Phonologically Impaired; CTRL = Controls; * p < .05. 

score, TONl-2 Quotient, and the three reading achievement standard scores (Word 
Attack, Word Identification, and Passage Comprehension) were entered into a stepwise 
regression analysis. No variables were selected as statistically significant predictor of 
AAT performance. This stepwise regression analysis was repeated for each group 
separately to determine if predictors of AAT performance differed by group. Again, no 
variables were selected as significant predictors of AAT score. Pearson correlation 
between the AAT score and each of the variables entered were presented in Table 20. 
The only variable from Table 20 that correlated with the AAT score was the LAC raw 
score, and this correlation was significant only for the CTRL group. 

Pearson correlations between the AAT score and the Phoneme Match response 
time and accuracy were also calculated to examine the relationship between articulatory 
knowledge and another task requiring phonological skills. The AAT score was positively 
correlated with Phoneme Match accuracy (Pearson's r = .64,/? = .00). Subjects with 
better articulatory knowledge were more accurate on the Phoneme Match Test. Dividing 



70 



Table 20. Pearson correlations between the AAT score and variables entered into 

stepwise regression analysis for PI and CTRL groups. 

Groups Combined PI CTRL 



Age 

BNT 

Rapid Color Naming 

Rapid Object Naming 

LAC : 

TONI-2 

WRMT Word Attack 
WRMT Word Identification 
WRMT Passage Comprehension 



.01 
.03 
.09 
.00 
.30* 
-.24 
.IS 
.15 
.11 



-.19 


.23 


-.16 


.35 


-.02 


.31 


-.11 


.28 


.18 


.40* 


-.25 


-.23 


.23 


-.00 


.16 


-.04 


.15 


-.18 



Note: PI = Phonologically Impaired; CTRL = ControfsrV ^="057 

subjects into groups showed that this correlation was significant only for the 
phonologically impaired group (Pearson's r = .74, p = .00). 

Predictors of Phonological Awareness 

Predictors of the LAC score were also explored with stepwise regression analysis. 
Age, BNT Z-score, Rapid Color Naming Z-score, Rapid Object Naming Z-score, TONI-2 
quotient, the three reading achie\ ement standard scores (Word Attack, Word 
Identification, and Passage Comprehension), and AAT raw score were entered. Table 21 
reported Pearson correlations between the LAC and each of these variables for the groups 
combined and for each group individually. Both groups' reading achievement scores 
were correlated with their LAC scores. However, because the inter-correlations between 



71 



the three achievement tests were high, only the most significant reading achievement 
score was selected to predict LAC performance in the regression analysis. For the CTRL 
group. Word Attack and age were selected as variables of predictive value. Word Attack 
alone contnbuted 27% (adjusted R') of the variance to the LAC score (F = 7.51,;, = .01). 
Word Attack and Age combined contributed 44% (adjusted R^) of the variance (F = 7.93, 
P = .00). For the PI group, Passage Comprehension and Rapid Object Naming were 
selected as variables of predictive value by the stepwise regression analysis. 
Surprisingly, the unique variance contnbuted by the BNT score was not significant once 
Passage Comprehension was entered. Instead, the variance contnbuted by Rapid Object 
Naming was deemed significant. The reverse con-elation between Rapid Object Naming 

Table 21 . Pearson con-elations between the LAC score and vanables entered into 





Groups Combined 


PI 


CTRL 


Age 




.39'" 


.29 


.48' 


BNT 




.34* 


.43* 


.07 


Rapid Color Naming 




.18 


.07 


.16 


Rapid Object Naming 




.04 


-.11 


.01 


TONI-2 




.01 


.27 


-.24 


WRMT Word Attack 




.61* 


.50' 


.55' 


WRMT Word Identification 




.59* 


.49* 


.44* 


WRMT Passage Comprehension 




.58* 


.55' 


.39* 


AAT 




.30* 


.18 


.40* 


Note. PI Phonologically Impaired; CTRL 


= Controls; * p 


' <= .05. 





72 
and the LAC indicated that the better PI subjects performed on the LAC, the slower they 

completed the Rapid Object Naming. Passage Comprehension alone contributed 27% 
(adjusted R^) of vanance to the LAC score (F - 8.31,;7 = .01). Passage Comprehension 
and Rapid Object Naming together contributed 42% (adjusted R^) of variance (F = 8.33, 
p = .00). 

As a check of LAC's validity as a measure of phonological awareness, the 
correlation between the LAC raw score and Phoneme Match Test performance was 
calculated. With all subjects combined, the correlation between the LAC score and the 
Phoneme Match performance was statistically significant (RT, Pearson's r = -31, p = .02; 
accuracy, Pearson's r = .48, p = .00). Dividing subjects into groups revealed that the LAC 
score and Phoneme Match performance was correlated for CTRLs (RT, Pearson's r = - 
M,p = .03; accuracy, Pearson's r = .69, p = .00) but not for the PI group (RT, Pearson's r 
= . 18, p = .44; accuracy, Pearson's r = .29,/? = .20). 

Developmental Phonological Ovslexirs vq 
Adequate Reader<; wi th Poor Phonology vs. Contrnk 

The PI group can be categorized into two distinct subgroups. As shown in Table 
1, the DPD group was characterized by impaired phonological processing, single-word 
reading, and comprehension (WRMT Word Attack, Word Identification, and Passage 
Comprehension respectively) in comparison to their expected achievement level based on 
their intellectual aptitude. The ARPP group, while demonstrating impaired phonological 
skills, actually has single-word reading and comprehension skills commensurate to their 
expected achievement level. This subtype of children with impaired phonological 



73 

processing but adequate reading ability has been described (Masutto & Comoldi, 1992), 

but little is known about them, such as whether these children represent a distinct subtype 
of dyslexia or a milder form of the disorder. To explore if the DPD and ARPP groups 
differed in their presentation on cognitive measures, differences between these groups 
were examined in this section. The CTRL group was also included in order to compare 
these two phonologically impaired groups with normal readers. 

Articulatory Awareness Te.st 

Articulatory Awareness Test scores obtained by DPD, ARPP, and CTRL groups 
were provided in Table 22. An ANOVA conducted with Group as the behveen-subject 
variable revealed that the three groups did not differ from each other on their AAT score 
(F = 0.41,/7 = 0.66). 

Table 22. Meansandstandarddeviationsof AAT scores obtained by DPD ARPP and 
C i KL groups. ' 



DPD ARPP CTRL 



AAT 6.0(2.72) 6.4 (IJS) " 6.7(1.75)" 



p:^ZnZ':cZr=?o^^^^^ ^^'^^^^^ ^^ = ^^^^-^^ ^-'- -^^ 



To examine if DPD and ARPP groups demonstrate different patterns of 
relationship between articulatory knowledge and name retrieval, Pearson correlations 
between AAT and reaction time dunng experimental conditions (i.e., NAPM and Visual 
Match interference conditions) were conducted for each group and reported in Table 23. 
Although the DPD group's correlations between their AAT score and naming latency 
appeared more similar to the CTRL group's and different from the ARPP group's, there 



74 
was no statistically significant difference between DPD and ARPP group's correlations 

on the NAPM interference conditions (Mouth Interference, Z(,,.,o) = -.S3,p> .05; Foot 

Interference, Z^uo) = --70, p> .05) or on the Visual Match interference conditions 

(Mouth Interference, Z(,,,,o) = -1.51,;? > .05; Foot Interference, Z^n,,o) = -.52, p> .05). 

Unlike the control group, whose correlation between naming latency and AAT score 

differed significantly from its corresponding correlation on the Visual Match Test dunng 

Mouth Interference (i.e., -.48 vs. . 1 9, Z,2o,2o> = -2.07, p < .05), no such corresponding 

correlation pairs within the DPD and ARPP groups were statistically different (DPD: 

NAPM Mouth vs. Visual Match Mouth, Zu,.,,) = -.06,/. > .05; NAPM Foot vs. Visual 

Match Foot, Z^u,) = -.6Q,p> .05; ARPP: NAPM Mouth vs. Visual Match Mouth, 

Z(io,io) = -J\,p > .05; NAPM Foot vs. Visual Match Foot, Z^io,io) = -39, p > .05). 



Table 23. Pearson correlations between the AAT score and reaction time 



on 





DPD 


ARPP 


CTRL 


NAPM 








Mouth Interference 


-.32 


.10 


-.48* 


Foot Interference 


-.29 


.06 


-.35 


Visual Match 








Mouth Interference 


-.29 


.45 


.19 


Foot Interference 

XT J. _ r-XTA»-v r^ 


-.00 


.26 


.27 



Note: DPD - Developmental Phonological Dyslexia; ARPP = Adequate Reader with 
Poor Phonology; CTRL = Controls; p < .05. 



75 

Descriptive Measures 



Table 24 showed each group's performance on descriptive measures. One-way 
analyses of variance indicated a group difference on the BNT (F = 4.66, p = .02) but not 
on any of the other naming measures (Rapid Color Naming, F = 2.89,/? = .07; Rapid 
Object Naming, F = 1 .54, ;? = .23; Naming Test, F = 2.30, p = . 11 ). On the BNT, 
independent samples t-test showed that the DPD group scored lower than the CTRL 
group (t = 2.74,p = .01). The ARPP group's BNT score did not differ from either of the 
other groups (ARPP vs. CTRL, t = 0.60,p = .55; ARPP vs. DPD, t = 1.85, ;; = .08). One- 
way analysis of variance on the LAC scores also indicated difference between groups (F 
= 7.7\,p = .00). Independent samples t-test showed that the DPD group's phonological 
awareness score was lower than both other groups (DPD vs. CTRL, t = 3.76, p = .00; 
DPD vs. ARPP, t = 2.84,;. = .01), while the ARPP group's LAC score did not differ from 
that of the CTRL group's (t = \.23,p = .23). 

JnH PT^T ^'^"' ^""^ '^^"'^''^ deviations on descriptive measures for the DPD ARPP 
and CTRL groups. ' ' 



DPD ARPP CTRL 



^^ ^7U2:52)^ Tl^ 

Rapid Color Naming^ .1.02 (L85) -0.08 (1.07) 0.02 (0.68) 

Rapid Object Nammg^ .,.25 (2.80) -0.49 (1.81) -0.05 (0.80) 

'''™"^^^^^' 85(7) 89(5) 90(6) 

^^^'^ 46(13)f 65(16)^ 75(23)^ 

roor ^nonology, CTRL - Controls. Z-scores. " Percentage correct ' Raw score 
Numbers ,„ each row w,* dWe.m superscnpts were s,gnilca„ti;diffe^nXm each 



76 

Experimental Measures 

Phoneme match. A MANOVA was conducted to see if groups differed in their 
reaction time on matching the end phoneme of words. Group (DPD, ARPP, and CTRL) 
was entered as the between-subject factor and Stimulus Set (A and B) was entered as the 
within-subject factor. The Group X Stimulus Set interaction was only marginally 
significam (F = 2.85, p = .07), but there was a significant Stimulus Set effect (F = 6.09, p 
= .02). The Stimulus Set effect was the same as that reported under the PI vs. CTRL 
section, where it was shown that subjects were faster in responding to Set A than to Set 
B. Each group-s response time to the Sets A and B were presented in Table 25. A 
similar analysis was conducted with response accuracy data. Only a marginal effect of 
group was found (F = 2.89,/, = .07). As there were no statistically significant Group 
effects on reaction time and response accuracy, it was assumed that the measures taken to 
counterbalance Stimulus Set with interference conditions dispersed differences between 
stimulus sets among different interference conditions, and no f\arther attempt to examine 
Stimulus Set's interaction with other variables was taken. 

Table 25. Reaction time and accuracy on the Phoneme Match Test for DPD ARPP and 
CTRL erouDs. ' ' " 



CTRL groups 



DPD ARPP CTRL 



EI % Correct Rt" %Co"n^ RT ^ %C^^ 

Set A 2958(633) 87(16) 2834(540) 93(6) 2758(445) 95(5) 
SetB 3285(1136) 87(14) 3156(793) 92(6) 2711(543) 94(7) 



77 

NAPM and visual match. Descriptive statistics for the experimental measures, 
NAPM and Visual Match, were presented in Table 26. Separate MANOVAs were 
conducted for reaction time and response accuracy data, with Group as a between-subject 
factor (DPD vs. ARPP vs. CTRL) and Task (NAPM and Visual Match), Interference 
(Mouth and Foot) and Block (1 and 2) as within-subject factors. Because this analysis 
was exactly the same as the analyses performed in the section on PI vs. CTRL, only 
effects or interactions involving Group (DPD vs. ARPP vs. CTRL) were reported here to 



Table 26. Reaction time and response accuracy on the NAPM and Visual Match Tests 
for DPD, ARPP, and CTRL groups. 





DPD 


i:^ 1_ 


ARPP 




CTRL 






RT 


% Correct 


RT 


% Correct 


RT 


% Correct 


NAPM 








' , « •- i 






Mouth 


4511 


77 


4179 


84 


3688 


88 




(1666) 


(17) 


(1472) 


(12) 


(1551) 


(10) 


Foot 


5117 


82 


4013 


85 


3653 


89 




(1510) 


(10) 


(1087) 


(5) 


(1135) 


(10) 


Visual Match 










\ 




Mouth 


4936 


76 


4957 


79 


5333 


86 




(1902) 


(17) 


(1756) 


(7) 


(1694) 


(13) 


Foot 


5243 


84 


4742 


83 


4984 


84 


Nntf • rjPFi - r 


(1528) 


(11) 

A_l r\i_ _ 


(1100) 


(7) 


(1719) 


(13) 



Poor Phonology; CTRL = Controls. 



78 

reduce redundancy. The MANOVA with reaction time data revealed a Group X Task 

interaction (F = 3.76,/? = .03). Pairwise comparisons using dependent samples t-test to 
follow up on the Group X Task interaction revealed that both ARPP and CTRL groups 
were faster in responding the to NAPM in comparison to the Visual Match (ARPP, t = - 
2.33, p = .04; CTRL, t = -4.5S,p = .00), but the DPD group's reaction time on these two 
tasks did not differ (t = -0.83,/? = .42). 

The MANOVA on accuracy data did not reveal any Group main effects or 
interactions. Other interactions and effects were exactly the same as those reported in the 
PI vs. CTRL section and were not repeated here. 

ADHD subjects . To see whether ADHD differentially affected the performance 
of the two phonologically impaired groups, reaction time and accuracy data of ADHD 
subjects were comrasted to those of non-ADHD subjects in Table 27. Dividing the DPD 
and ARPP groups into ADHD vs. Non-ADHD subgroups dramatically reduced the 
number of subjects in each subgroup, rendering multivariate analyses looking at 
differences between groups unrealistic due to low power. Nevertheless, perusing Table 
27 suggested that ADHD subjects tended to be less accurate than their non-ADHD 
counterparts. 

Interfenn^ movements . To examine if the two phonologically impaired groups 
engaged in mouth and foot interfering movements with equal facility, a MANOVA was 
conducted with the interfenng movement frequency data. The CTRL group was included 
in this analysis as a comparison group. Group (DPD vs. ARPP vs. CTRL) was entered as 
a between-subject factor and Task (NAPM and Visual Match), Interference (Mouth and 
Foot), and Block (1 and 2) were entered as within-subject factors. Table 28 showed each 



79 



Table 27. Reaction time and accuracy of the phonologically impaired Non-ADHD and 
ADHD subgroups. 



Non- 





ADHD 






(n = 5) 




JAPM 


RT 


% 
Correct 


»l/\rlVl 


4581 


80 


4outh 


(356) 


(13) 


Foot 


5551 


87 



DPD 



ADHD 



(n = 6) 



RT 



ARPP 

Non- 
ADHD 

(n = 7) 

% RT % 

Correct Correct 



ADHD 



(n = 3) 



RT 



4454 



75 



3772 



87 



5130 



(13) (2333) (21) (1468) 
4756 77 3716 



% 



Correct 



78 



(11) (1164) (14) 
86 4705 84 



(1951) (9) (1081) (9) (1036) (6) (1035) (1) 
Visual 
Match • 

5145 83 4761 69 4899 81 

Mouth (1285) (8) (2415) (21) (1782) (8) 

Foot 5290 84 5204 84 4667 81 

(1212) (8) (1868) (14) (1258) (8) 



5092 75 

(2076) (3) 

4917 88 

(797) (7) 



Note: DPD = Developmental Phonological Dyslexia; ARPP = Adequate Reader with 
Poor Phonology. 



group's interference movement frequency index. The result of this analysis was similar 
to the analysis with the two phonologically impaired groups combined, with the 
exception that the four-way interaction (Group X Task X Interference X Block) was now 



80 

only marginally significant (F = 2.61, p = .09). Because breaking the PI group down into 

the DPD and ARPP groups reduced the number of subjects in these subgroups, the power 
to detect a four- way interaction in the present analysis if one existed was reduced. 
Interestingly, an examination of Table 28 revealed that ARPP subjects appeared to 
produce interference movements with greater facility than DPD subjects. The exception 



Table 28. Interfering movement frequency index for the DPD, ARPP, and CTRL groups. 
DPD ARPP CTRL 



NAPM 




^ 




Mouth 


.83 (.25) 


.87 (.25) 


.97 (.32) 


Block 1 


.83 (.27) 


.85 (.23) 


.98 (.35) 


Block 2 


.83 (.25) 


.89 (.28) 


.95 (.31) 


Foot 


.90 (.19) 


1.11 (.27) 


1.01 (.26) 


Block 1 


.88 (.20) 


1.08 (.27) 


.97 (.26) 


Block 2 


.91 (.17) 


1.15 (.30) 


1.05 (.27) 


Visual Match 








Mouth 


.94 (.20) 


1.04 (.24) 


.99 (.23) 


Block 1 


.93 (.18) 


1.05 (.29) 


.98 (.25) 


Block 2 


.95 (.23) 


1.04 (.20) 


1.01 (.24) 


Foot 


.96 (.17) 


1.11 (.31) 


1.04 (.30) 


Block 1 


.95 (.18) 


1.07 (.33) 


1.05 (.32) 


Block 2 


.97 (.18) 


1.16 (.30) 


1.03 (.30) 



Note: DPD - Developmental Phonological Dyslexia; ARPP = Adequate Reader with 
Poor Phonology; CTRL = Controls. 



to this was during NAPM Mouth Interference, where both DPD and ARPP subjects 
produced interfenng movements with approximately equivalem facility, especially during 
Block L Compared to the CTRL group, the ARPP subjects tended to produce interfenng 
movements with greater facility, except durrng the NAPM Mouth Interference conduion, 
whereas the DPD subjects tended to be slower in producing interfenng movements than 
the CTRL group Companng Mouth Interference to Foot Interference, the ARPP group 
appeared to have the greatest discrepancy between these interfeience conditions during 
NAPM, while neither of the other groups showed such discrepancy on either NAPM or 
Visual Match. 

To examine how interference movement frequency related to each group's 
articulatory knowledge, Pearson correlations between the AAT score and interfering 
movement frequency index for each group were calculated and reported in Table 29. The 
positive correlation between the AAT score and the NAPM Mouth Interference 
movement index was driven by the DPD group. The better the DPD group's articulatory 
knowledge, the faster they were able to produce interfering mouth movements during a 
name retrieval task. The negative correlation between the AAT score and the Visual 
Match Foot Interference movement index was driven by the ARPP group. The better the 
ARPP group's articulatory knowledge, the more slowly they produced foot interference 
movements during a nonverbal visual task. 

Predictors of Articulatory t^nn»,i»^^p 

To explore if variables that predicted the AAT score differed for the DPD and 
ARPP groups, a stepwise regression analysis was conducted with age, BNT Z-score, 



82 
Table 29. Pearson correlations between the AAT score and interfering movement 
frequency index for DPP, ARPP, and CTRL groups . 





DPD 


ARPP 


CTRL 


NAPM 








Mouth Interference 


.74* 


.06 


-.14 


Foot Interference 


.28 


-.52 


-.24 


Visual Match 








Mouth Interference 


.16 


-.24 


-.14 


Foot Interference 


-.47 


-.64' 


-.13 



Note: DPD - Developmental Phonological Dyslexia; ARPP = Adequate Reader with 
Poor Phonology; CTRL = Controls. ' p < .05. 



Rapid Color Naming Z-score, Rapid Object Naming Z-score, LAC raw score, TONI-2 
Quotient, and the three reading achievement standard scores (Word Attack, Word 
Identification, Passage Comprehension) entered as independent factors. Pearson 
correlations between the AAT score and each of these variables for DPD and ARPP 
groups were presented in Table 30. The CTRL group's correlation between AAT and 
each independent variable were also provided for comparison. For the DPD group, no 
variable was correlated with the AAT. Consequently no variable was selected by the 
stepwise regression as a predictor of the DPD group's AAT score. For the ARPP group, 
the three reading achievement measures, which were highly correlated with each other, 
were positively correlated with AAT score. A perusal of scatter plots of reading 
achievement scores as a function of AAT scores indicated that these significant 
correlations were valid and not due to the presence of extreme scores. The Passage 
Comprehension standard score was selected by the stepwise regression analysis and 



A 



accounted for 52% (adjusted R^) of the variance to the ARPP group's AAT score (F = 
10.59, p = .0\). The correlation between the ARPP group's AAT score and reading 
achievement measures indicated that the better their articulatory knowledge, the higher 
reading attainment ARPP subjects were able to achieve. 



83 



Table 30. Pearson correlations between the AAT score and variables entered into 





DPD 


— f"- 
ARPP 


CTRL 


Age 


-.13 


-.32 


.23 


BNT 


-.24 


-.13 


.35 


Rapid Color Naming 


-.22 


.45 


.31 


Rapid Object Naming 


-.28 


.29 


.28 


LAC 


.20 


At 


.40* 


TONI-2 


-.47 


M 


-.23 


WRMT Word Attack 


.02 


.6/ 


-.00 


WRMT Word Idemification 


-.04 


M* 


-.04 


WRMT Passage Comprehension 


-.03 


.76' 


-.18 



Note: DPD - Developmental Phonological Dyslexia; ARPP = Adequate Reader with 
Poor Phonology; CTRL = Controls; V <= .05. 



Other than the variables examined in Table 30, Pearson correlations between the 
AAT score and Phoneme Match performance were also examined. Both DPD and ARPP 
groups' AAT scores were significantly correlated with their accuracy on the Phoneme 
Match Test (DPD: r = .77,p = .01; ARPP: r = .76,/, = .01). The better their articulatory 
knowledge, the more accurate they were in deciding if phonemes matched. 



84 

Predictors of Phonological Awareness 



Predictors of the LAC score for DPD and ARPP groups were also explored with 
stepwise regression analysis. Age, BNT Z-score, Rapid Color Naming Z-score, Rapid 
Object Naming Z-score, TONI-2 quotient, the three reading achievement standard scores 
(Word Attack, Word Identification, Passage Comprehension), and AAT raw score were 
entered as independent factors. Table 3 1 showed Pearson correlations between the LAC 
score and each of these variables for DPD and ARPP groups. The CTRL group's 
correlations were also listed for comparison. When the phonologically impaired group 
was divided into DPD and ARPP groups, power to detect correlations was decreased such 
that the previously significant correlations between the LAC score and reading 
achievement measures were no longer significant for either the DPD or ARPP group. 
The negative correlation between the LAC and Rapid Color Naming was significant for 
the ARPP group only. The better their phonological awareness, the more slowly ARPP 
subjects were able to complete rapid naming, especially of colors. No variables were 
selected as variables of predictive value by stepwise regression analysis for either DPD or 
ARPP groups. 

Correlation between the LAC raw score and response time on the Phoneme Match 
Test was calculated for the two phonologically impaired groups. Neither the DPD nor 
ARPP group's LAC score was correlated with their performance on the Phoneme Match 
Test (DPD: RT, Pearson's r = -.2A,p = .49; accuracy, Pearson's r = .28, /? = .41; ARPP: 
RT, Pearson's r = -.08,/? = .84; accuracy, Pearson's r = .08,/> = .84). 



Table 3 1 . Pearson correlations between the LAC score and variables entered into 
stepwise regression analysis for DPP and ARPP groups. 

DPD ARPP CTRL 



85 



Age 


""-^07 


.44 


.48* 


BNT 


.24 


.50 


.07 


Rapid Color Naming 


.18 


-.61* 


.16 


Rapid Object Naming 


-.08 


-.50 


.01 


TONI-2 


.14 


.09 


-.24 


WRMT Word Attack 


.42 


.18 


.55* 


WRMT Word Idemification 


.26 


.01 


.44* 


WRMT Passage Comprehension 


.24 


.24 


.39* 


AAT 


.20 


.12 


.40* 



Note: DPD - Developmental Phonological Dyslexia; ARPP = Adequate Reader with 
Poor Phonology; CTRL = Controls; ' p<= .05. 



Poor vs. Adequate Articulatorv Knnwiedp p 



The articulatory feedback hypothesis of naming hypothesized that articulatory 
feedback facilitates name retrieval. This hypothesis makes the assumption of the 
presence of articulatory knowledge. There was theoretical interest in examining the 
name retrieval process of those subjects with adequate articulatory knowledge and those 
with inadequate articulatory knowledge. Presumably, the hypothesis may hold true for 
those with adequate articulatory knowledge but not for those with poor articulatory 
knowledge. To group subjects imo poor vs. adequate articulatory knowledge groups, the 
mean AAT score for the entire population of subjects was calculated, and one standard 
deviation below the mean, which corresponded to a Z-score of -1, was selected as the 



86 

cutoff score for grouping criterion. The mean AAT score of all 41 subjects was 6.44 with 

a standard deviation of 2.03. Thus subjects with an AAT score of 4 or below were 
grouped into the Poor Articulatory Knowledge group (PAK), and those with an AAT 
score of 5 or above were grouped into the Adequate Articulatory Knowledge group 
(AAK). Table 32 reported some descriptive statistics about each subject group. 
Independent samples t-test mdicated that the two groups did not differ in age (t = -0. 16,/7 
= .88) or intellectual aptitude as estimated by the TONI-2 (t = -\A6,p = .15). All of the 
subjects in the PAK group were males, and the majonty of this group was composed of 
children with ADHD. Note there was unequal distribution of the number of subjects in 
each group, with only seven subjects falling into the PAK group. 

IS!f f. °!!"''^fPl''" ""^^^^ ^^^^ ^'^'^"'atory Knowledge (PAK) and Adequate 
Articulators Knowledge (AAK) groups ^ 

PAK AAK 
(n = 7) (n = 34) 



Age 9 (2) 9{2j~" 

TONI-2 111(7) 105(10) 

M:F Ratio 7:0 20:14 
ADHD 5 7 



Descriptive Measures 



The perfomance of PAK and AAK groups on descriptive measures was 
examined for group differences^ Tabic 33 summanzed the groups' performance on these 
measures. Thetc was no difference between groups on any of the naming measures when 



87 

group differences were tested using independent samples t-test (BNT, t = -0.18,/? = .86; 

Rapid Color Naming, t = 0.84,/? = .41; Rapid Object Naming, t = 0.23,p = .82; Naming 
Test, t = 1 . 1 5, /? = .26). Independent samples t-test indicated that the PAK group scored 
more poorly on the LAC than the AAK group (t = 2A3,p = .02), not surprisingly as LAC 
score was significantly correlated with AAT score (Table 20). Comparison of mean 
standard scores between groups using independent samples t-test revealed that the PAK 
group scored lower on Word Attack (t = 2.76,p = .01) and Word Identification (t = 2.27, 
p = .03) compared to AAK, but not on Passage Comprehension (t = \.77,p = .08). 

Table 33. Means and standard deviations on descriptive measures for the PAK and AAK 
groups. 

PAK ■ AAK 



BNT" 


-1.31(1.83) 


-1.44(1.83) 


Rapid Color Naming' 


-.65 (.72) 


-.21 (1.33) 


Rapid Object Naming' 


-.63 (.87) 


-.46(1.97) 


Naming Test'' 


86(5) 


89(6) 


LAC° 


47 (22)* 


68(21)'' 


WRMT Word Attack*' 


74(12)' 


92(17)'' 


WRMT Word Identification'' 


77(13)' 


94 (19)'' 


WRMT Passage Comprehension'' 


83(14) 


95(17) 



Note. PAK - Poor Articulatory Knowledge; AAK = Adequate Articulatory Knowledge 

each row numrr''fHHTr' ^^"^^^^^- ' Age-corrected standard scores. Within 
each row, numbers with differem superscripts were significantly differem from each 



as 



88 

Experimental Measures 



Phoneme match. Performance on the Phoneme Match Test was examined to see 
if PAK and AAK groups differed in their ability to match phonemes. Separate ANOVAs 
were conducted for reaction time and response accuracy with Group (PAK vs. AAK) 
the between-subject variable. Table 34 showed each group^s performance on the 
Phoneme Match Test. The PAK group was both slower in reaction time (F = 5.45,;? 
.02) and less accurate (F = 66.91, p = .00) than the AAK group. The overall AAT 
and Phoneme Match reaction time was not correlated (i.e., all subjects combmed; 
Pearson's r = -26, p = .10). However, the AAT score did correlate positively with 
Phoneme Match accuracy (Pearson's r = M,p = .00). Because the PAK group's ability 
to match phonemes was remarkably worse, their reaction time and accuracy on the 
Phoneme Match Task were used as covariates in analyses involving NAPM because the 
NAPM required phoneme match as an integral part of the task. 



score 



Table 34. Reaction time and accuracy on the Phoneme Match Test for the PAK and 
AAK groups. 

PAK AAK 



Reaction time 3385 ( 1 18) 2802~(490) 
% Correct 76(11) 95(4) 



Note: PAK - Poor Art.culatory Knowledge; AAK = Adequate Articulatoiy Knowledge. 

NAPM and visual match . Subjects' reaction time and accuracy on the NAPM 
were reported in Table 35, and their Visual Match performance were reported in Table 
36. Separate analyses were conducted with NAPM and Visual Match because Phoneme 
Match performance was used as a covanate in analyzing NAPM data while the use of this 



89 

covariate would be inappropriate for Visual Match because phoneme match was not 

required as part of the Visual Match Test. Table 35 represented subjects' scores without 
the covariate extracted. - 



Table 35. Reaction time and accuracy on the NAPM for PAK and AAK groups 
Numbers represent data without the covariate extracted. 

PAK ■ " XaK 



SI % Correct RT % Correct 

Mouth 

Block 1 5795(2460) 70(17) 3836(1541) 86(12) 

Block2 5142(2113) 79(18) 3659(1151) 87(14) 
Foot ' 

Block 1 5838(1671) 82(12) 3881(1151) 87(10) 

Block 2 4904(1687) 85(10) 3888(1275) 85(12) 



Note; PAK - Poor Articulatory Knowledge; AAK = Adequate Articulatory Knowledge. 

On the NAPM (Table 35), separate MANCOVAs were conducted for reaction 
time and response accuracy data. Group (PAK vs. AAK) was the between-subject factor, 
Interference (Mouth and Foot) and Block (1 and 2) were within-subject factors, and 
Phoneme Test reaction time and response accuracy were covariates in MANCOVAs 
analyzing reaction time and response accuracy, respectively. The reaction time 
MANCOVA revealed a significant Group X Interference X Block interaction (F = 4.33,;. 
= .04), an Interference X Block X Covariate interaction (F = 6.65, p= .01), an 
Interference X Block interaction (F = 6.57,;, ^ .01), and a Group X Block interaction (F 
= 4.43,;? = .04). The Group X Interference X Block interaction was explored by 



90 

separate MANOVAs for each group with Interference and Block as within-subject 

variables. These analyses indicated that the PAK group's reaction time improved in 
Block 2 (F = 17.02,/? = .01) while the AAK group's did not (F = 0.48,/? = .49). 
Dependent samples t-tests to compare the PAK group's Block 1 and Block 2 reaction 
time for each interference condition revealed that the improvement in Block 2's reaction 
time was more salient during the Foot Interference condition (t = 3.85,/? = .01) than 
during the Mouth Interference condition (t = 2.14,/> = .08). 

The Interference X Block X Covariate interaction was explored by conducting a 
MANOVA with Interference as the within-subject variable and Phoneme Match response 
time as the covariate for each Block. These analyses revealed an Interference X 
Covanate interaction for Block 1 (F = 6.07,/? = .02; Figure 2) that did not exist for Block 
2 (F = \M,p = .21; Figure 3). In Block 1 (Figure 2), the slower the subjects' response 
time on the Phoneme Match Test (i.e., poorer the phoneme match ability), the more 
slowly they responded during the Mouth Interference condition compared to the Foot 
Interference condition. This effect went away by Block 2 (Figure 3). The MANCOVA 
on the NAPM accuracy data revealed no statistically significant interactions or effects, 
which rendered the reaction time data free from speed-accuracy trade off. 

Separate reaction time and response accuracy analyses were conducted for Visual 
Match (Table 36). Again, Group (PAK vs. AAK) was the between-subject factor and 
Interference (Mouth and Foot) and Block (1 and 2) were within-subject factors. The 
MANOVA on reaction time data revealed only a Block effect (F = 5.81,p = .02). 
Subjects were faster during Block 1 than Block 2. The MANOVA on accuracy data 
revealed a significant Interference X Block interaction (F = 7.38,/? = .01) and 



91 



a: 

< 
Z 



9500 - 
8500 


Q^^-'. 


♦ 


7500 -j 


♦ 




6500 - 
5500 - 




e 


4500 - 
3500 - 






2500 - 
1500 - 







-^i^">^ -l^ i 



1500 2500 3500 4500 5500 
Phoneme Match RT 



♦ Mouth Interference 
« Foot Interference 



Figure 2. Block 1 NAPM reaction time, plotted against the ability to match end phonemes 



92 



9500 - 

8500 - 

^ 7500^ 

^ 6500 H 



< 



5500 -| 
4500 ^ 
3500 - 
2500 - 
1500 i 



f ♦ 



^ 






1500 2500 3500 4500 5500 
Phoneme Match RT 



♦ Mouth Interference 
= Foot Interference 



Figure 3. Block 2 NAPM reaction time, plotted agamst the ability to match end phonemes 



-. ' •< i« . 



93 

Interference (F = 6.57,/? = .01) and Block main effects (F = 5.95, p = .02). Follow up 

pairwise comparisons using dependent samples t-test showed that subjects became less 
accurate dunng Block 2 on Mouth Interference (t = 221,p = .00), but there was no 
accuracy difference between Block 1 and Block 2 dunng Foot Interference (t - 1.42,/? = 
.16). 



Table 36. Reaction time and accuracy on the Visual Match Test for PAK and AAK 
groups. 



PAK AAK 



EI % Correct RT % Correct 

Mouth 

Block 1 5065(1887) 82(7) 4809(1488) 85(15) 

Block 2 5088(1927) 70(18) 5455(2022) 78(18) 

Foot 



Block 1 


4640(1637) 


84(14) 


4766(1407) 


86(12) 


Block 2 


5017(1735) 


86(10) 


5305(1736) 


83(12) 



Note: PAK - Poor Articulatory Knowledge; AAK = Adequate Articulatoiy Knowledge. 

Interference movements . The frequency of interference movements produced 
during Mouth and Foot Interference conditions were analyzed to see if PAK and AAK 
groups produced these movements with equal facility. A MANOVA with Group (PAK 
vs. AAK) as the betxveen-subject factor and Task (NAPM and Visual Match), 
Interference (Mouth and Foot), and Block (1 and 2) as withm-subject factors was 
conducted. This revealed the following significam interactions and main effects: Group 
X Task (F = 7.88,;, = .01), Group X Interference (F = 5.82,;, = .02), Task X Interference 



94 

(F = 5.51, p = .02), Task main effect (F = 9.51,p = .00), Interference main effect (F = 

10.91,/; = .00), and Block main effect (F = 5.16,/? = .03). Subjects produced interfering 
movements at a greater frequency during Block 2 than Block 1 . Because Block did not 
interact with any other vanables, mean movement frequencies reported in Table 37 were 
collapsed across the two blocks. Dependent samples t-tests to examine the nature of the 
Group X Task interaction indicated that the PAK group produced movements at a slower 
rate during NAPM than during Visual Match (t = -4.50, p = .00). The AAK group did 
not demonstrate a difference in the frequency of movements produced across these two 
tasks (t = -.33,/? = .74). Dependent samples t-tests to examine the Group X Interference 
interaction revealed that the PAK group tended to be slower in producing interfering 
movements during Mouth Interference than during Foot Interference (t = 1.06, p = .09). • 
The AAK group produced interfering movements with equal facility across the two 
interference conditions (t = 1.24,/; = .22). Dependent samples t-tests to examine the 
Task X Interference interaction revealed a difference between Mouth and Foot 
Interference on the NAPM (t = 2.39, p = .02) but not on Visual Match (t = 1.21,/; = .23). 
Interfering foot movements were produced %vith greater facility than interfenng mouth 
movements during the NAPM task. In combination, these three interactions reflected the 
trend that the PAK subjects tended to produce interfenng movements most slowly during 
the Mouth Interference condition of the NAPM. This would be represented by a three- 
way interaction between Group, Task, and Interference, which was marginally significant 
in the overall MANOVA analyzing interfering movemem frequency (F = 3.58,/; = .07). 
The power to detect this three-way interaction was limited (power = .46). The effect size 
of this Group X Task X Interference interaction was calculated using the formula in 



95 

Figure 4, which yielded, ([(-68- 1.02)-(.95 - 1.00)] - [(1.06- 1.18)-(.98- 1.01)]}/.26 



.77. 



Table 37. Interfenng movement frequency index for the PAK and AAK groups 
PAK AAK 



NAPM 

Mouth .68 (.14) .95 (.29) 

Foot 1.02 (.34) 1.00 (.23) 

Visual Match . ? . 

Mouth 1.06 (.21) .98 (.23) 

Foot 1.18 (.38) 1.01 (.24) 



Note: PAK - Poor Articulatory Knowledge; AAK = Adequate Articulatory Knowledge. 



96 

ES = {[NAPM, PAK(Mouth - Foot) - NAPM, AAK(Mouth - Foot)] - 

[Visual Match, PAK(Mouth - Foot) - Visual Match, AAK(Mouth - Foot)]] 
/Pooled standard deviation, . " 



Figure 4. Formula for calculating the effect size reflecting the Group X Task X 
Interference interaction. 



DISCUSSION 



Review of Hypothesis 



To examine the relationship between articulatory knowledge and name retrieval, 
phonologically impaired readers were chosen as experimental subjects because of the 
literature documenting their naming deficits and impaired articulatory knowledge. The 
literature on the naming ability of dyslexic individuals indicated that in addition to their 
problems with reading comprehension, they also have problems with name retrieval 
(Swan & Goswami, 1997; Fawcett & Nicolson, 1994). Their retrieval deficit was not due 
to problems of intelligence or vocabulary. When given multiple choices, dyslexics as a 
group was better at correctly recognizing a target item although they failed at naming that 
item (Wolf & Obregon, 1992). This pattern of performance contrasted with that of 
garden-variety poor readers, who usually have lower vocabulary scores and do not 
perform as well as dyslexics in recognizing target items from a choice of options. Swan 
and Goswami (1997) captured this pattern of naming performance well by describing 
dyslexic children's poor naming as due to retrieval problems and garden-variety poor 
readers' poor naming as due to limited vocabulary. Name retrieval difficulties have been 
shown to be evident in dyslexics' narrative speech as well as on formal 
neuropsychological measures (Murphy et al., 1988). Dyslexic children's naming 
difficulties persisted into adulthood (Korhonen, 1995; Fawcell & Nicolson, 1994; Felton 



97 



98 

et al., 1990). That is, their naming difficuhies were deficits that did not go away with 

maturation. 

In an intriguing study, Montgomery (1981 ) found that dyslexic children tended to 
have worse knowledge of their articulators' positions than normal readers. This was 
important because it raised the question of how articulatory knowledge and language 
functioning are related. Some treatment programs for reading disability included 
protocols to train readers to be more aware and sensitive to the position of their 
articulators while making speech sounds. The success of such programs suggested that 
articulatory knowledge and reading are related (Alexander, Andersen, Heilman, Voeller, 
& Torgesen, 1991; Oakland, Black, Stanford, Nussbaum, & Balise, 1998). Among 
normal readers, there may be a relationship between articulatory awareness, or 
spontaneous use of articulatory knowledge during linguistic activities, and reading. The 
existence of such a relationship has support from neuroanatomical studies showing that 
dyslexic individuals have a higher rate of abnormality in the regions surrounding or 
involving the Heschl's gyrus and planum temporale (Hund et al., 1990; Larsen et al., 
1990; Leonard et al., 1993). These are posterior language regions important for the 
perception of speech and phonological representations of words. Metabolic studies also 
pointed to abnormal activation of anterior and posterior language regions among dyslexic 
individuals while they performed language tasks (Paulesu et al., 1996). These evidences 
led to questions about the nature of the relationship between articulatory knowledge and 
name retrieval. . - > ^ . » - v ■ • • 

The articulatory feedback hypothesis of naming stated that having articulatory 
feedback facilitates the name retrieval process. Without articulatory feedback, name 



99 

retrieval may be less efficiently achieved. To test this hypothesis, this study utilized an 

interference paradigm during a name retrieval task. An interference task was introduced 
(i.e.. Mouth Interference) such that articulatory feedback appropriate to the name retrieval 
task cannot be generated. Because subjects' mouths were engaged in an interference task, 
naming had to be assessed through phoneme match (NAPM). Naming reaction time and 
accuracy of the control subjects were contrasted with naming performance of those with 
poor articulatory knowledge. If there is support for the articulatory feedback hypothesis 
of nammg, taking away appropriate articulatory feedback by introducing an interference 
task should negatively affect the control group's performance but not the performance of 
groups with impaired articulatory knowledge. A condition (Foot Interference) was 
introduced to control for general interference effects of engaging in multiple tasks at 
once. A non-verbal task (Visual Match) was included to control for a potential difference 
between the attentional demand of the two interfering conditions (Mouth and Foot 
Interference). 

In this discussion of the results, the general relationship between articulatory 
knowledge and name retrieval was discussed first. Then that relationship was further 
elucidated by examining group differences. Three different grouping methods were 
employed to examine differences in the pattern of responses on experimental measures 
(i.e., NAPM and Visual Match). The performance of the Phonologically Impaired (PI) 
group was first contrasted against the performance of normal reader control subjects 
(CTRL). Then the Phonologically Impaired group was divided into two subgroups. 
Developmental Phonological Dyslexics (DPD) and Adequate Readers with Poor 
Phonology (ARPP). Finally, level of articulatory knowledge was examined more directly 



100 
by dividing groups into Poor Articulatory Knowledge (PAK) and Adequate Articulatory 

Knowledge (AAK) groups based on their performance on the AAT. As secondary 
explorations, the influence of subjects with ADHD and the relationship between 
articulatory knowledge and phonological awareness were also addressed. Finally, this 
discussion terminated with a modification of the articulatory feedback hypothesis of 
naming. 

Correlation Between Articulatory Knowledge and Name Retrieval 

The articulatory feedback hypothesis of naming predicted that Mouth Interference 
would interfere with appropriate feedback for naming, and therefore the better knowledge 
one had of one's articulator positions, the more slowly one would perform on our naming 
task. This would manifest in a non-significant or a positive correlation between 
articulatory knowledge and response latency. In contrast, a negative correlation 
found indicating faster naming latency with better articulatory knowledge despite 
interference with appropriate feedback (Table 6). The hypothesis predicted that subjects 
with better articulatory knowledge would use appropriate articulatory feedback during the 
Foot Interference condition to facilitate name retrieval. This would manifest in a 
negative correlation between articulatory knowledge and naming latency during Foot 
Interference. Instead, a non-significant correlation was found indicating that subjects did 
not spontaneously use articulatory feedback to assist retrieval. 

Disregarding the direction of correlations for a moment to consider the correlation 
that was statistically significant, findings indicated that a significant correlation between 
articulatory knowledge and naming latency but not between articulatory knowledge and 



was 
te 



101 

visual match suggested a relationship between articulatory knowledge and name retrieval 

(Table 6). Dividing subjects into phonologically impaired and control groups revealed 
that this pattern was driven by the control group (Table 8). For normal readers, their 
level of articulatory knowledge was correlated with their naming performance but not 
with their performance during a nonlinguistic control task. This indicated the existence 
of a relationship between articulatory knowledge and name retrieval among normal 
readers. 

The direction of the correlation between articulatory knowledge and each of the 
two interference conditions was unanticipated. Normal readers did not appear to use 
articulatory feedback in a spontaneous fashion to facilitate name retrieval, as their 
naming latency was not correlated with articulatory knowledge when articulators were 
free to provide feedback (i.e.. Foot Interference). But when their attention was drawn to 
their articulators, better articulatory knowledge was accompanied by faster naming 
latency (i.e.. Mouth Interference). Their articulators were engaged in interfering mouth 
movements at this time; therefore articulatory feedback was not complimentary to name 
retrieval. Despite irrelevant articulatory feedback, naming latency decreased as 
knowledge level of articulator^' position increased. The decreasing naming latency could 
not have been due to specific articulatory feedback. It must have been due to some other 
factor related to articulatory movement. The act of moving articulators required the 
activation of the primary motor and premotor cortices controlling them. These areas are 
also activated by the language system. One possibility is that activation of these cortical 
areas resulted in the "spreading" of activation to connected language systems, in much 
the same way as Collins and Loftus' (1975) spreading activation model explained 



102 
semantic activation of related concepts from activation of one concept. Normal readers 

with better articulatory knowledge may have more efficient connectivity of these 
systems. Thus the better their articulatory knowledge, the more efficiently activation 
could spread from motor cortices to the name retrieval system and engage that system. 

The phonologically impaired subjects did not demonstrate any significant 
correlation between articulatory knowledge and name retrieval. Regardless of how good 
their articulatory knowledge when directly inquired about it (i.e., AAT score), they did 
not use this information spontaneously to help them with name retrieval, as their naming 
latency was not correlated with articulatory knowledge when articulators were free to 
provide feedback (i.e.. Foot Interference). When their articulators were not able to 
provide feedback due to engagement in interfering movements, the phonologically 
impaired subjects' articulatory knowledge was also not correlated with naming latency. 
Dividing the phonologically impaired subjects into impaired (i.e., DPD) and non- 
impaired (i.e., ARPP) readers showed that the impaired readers' pattern appeared to be 
more similar to that of the normal readers. While the impaired readers' articulatory 
knowledge appeared to be correlated with naming latency, non-impaired readers did not 
show such a relationship (see Table 23). However, the impaired readers' correlation 
between articulatory knowledge and naming latency was not statistically significant, and 
the correlations of the two phonologically impaired groups were not statistically different 
from one another. The lack of a significant difference between impaired and non- 
impaired readers' correlations may have been due to the small number of subjects in each 
group (DPD, n = 11; ARPP, n = 10). 



103 
Group Differences in Articulatory Knowledge 

Montgomery's (198 1 ) finding that dyslexic children have worse articulatory 
knowledge compared to normal readers was not wholly supported by the present study. 
While the difference between the phonologically impaired and control groups on the 
AAT was in a direction consistent with Montgomery's findings (see Table 7), both groups 
demonstrated significant variability in their articulatory icnowledge levels as assessed by 
AAT. The control group's score on the AAT ranged from 2 to 9 (maximum score was 
10). The phonologically impaired group's score ranged from 1 to 9. Some normal 
readers have poor articulatory knowledge. Some phonologically impaired readers have 
good articulatory knowledge. Dividing the phonologically group into impaired and non- 
impaired reader groups did not change results. Again, the pattern of mean scores 
obtained by the three groups was consistent with Montgomery's findings, with the 
impaired readers obtaining the worst score and the normal readers obtaining the best 
score (Table 22). However, the variability demonstrated by all three groups precluded 
the finding of statistical difference among groups on articulatory knowledge. 

The data suggested that articulatory knowledge was neither necessary nor 
sufficient for achieving reading skills. It is intriguing, however, that the impaired readers 
obtained the worst score and the normal readers the best score, with the non-reading 
impaired but phonologically impaired subjects falling in betvveen. The impaired readers 
have the worst reading achievement, followed by the adequate readers with poor 
phonology, followed by the control subjects, who have age-appropriate reading 
achievement scores. The congruence of this pattern with Montgomery's (1981) finding 
raised questions about methodological differences between these two studies and possible 



104 
factors contributing to the present finding of null significance. Montgomery's study 

included 34 subjects in each of the dyslexic and normal reader groups. These data were 
collected over two different time periods, and she altered the instruction given to her 
second set of subjects by explicitly going over positions of each articulatory organ drawn 
in her sagittal cartoons. This change in instruction reduced the difference between group 
scores by increasing the scores obtained by the dyslexic subjects. The instruction to the 
Articulatory Awareness Test used in the present study resembled Montgomery's second 
set of instructions. Familiarizing subjects to the task by explicitly pointing out the 
position of articulator depicted in drawings may have "taught" dyslexic subjects how to 
do the task. The AAT is an experimental measure that has not undergone rigorous testing 
and revision with large subject samples to validate it as an instrument capable of 
detecting differences in articulator^ knowledge between groups. It is possible that the 
AAT, in its present form, is not sensitive enough to detect differences between groups 
even if differences exist. 

Group Diffe rences on Naming Measures 

On visual confrontation naming (BNT), phonologically impaired subjects tended 
to perform worse than normal readers (Table 9), although this group difference was not 
significant unless the phonologically impaired group was further divided into impaired 
reading and non-impaired reading groups. The impaired readers' confrontation naming 
was worse than that of the controls', but the non-impaired readers' confrontation naming 
did not differ statistically from either of the other groups (Table 24). Wolf and Obregon 
(1992) and Swan and Goswami (1997) both found that dyslexic subjects have worse 



105 
confrontation naming than normal readers. Both groups recruited dyslexic subjects who 

have documented reading impairments. Among the present subject groups, although the 
Adequate Reader with Poor Phonology group have impaired phonological skills, their 
smgle-word reading and reading comprehension were actually commensurate with their 
expected achievement scores given their estimated intellectual aptitude. Only the 
Developmental Phonological Dyslexia group in the present study had impaired single- 
word reading and reading comprehension in comparison to their expected achievement 
levels. Therefore, it was appropriate to compare the Developmental Phonological 
Dyslexia group but inappropriate to compare the Adequate Reader with Poor Phonology 
group with the findings reported by Wolf and Obregon (1992) and Swan and Goswami 
(1997). The present finding of impaired BNT score in the impaired readers (DPD) was 
consistent with these researchers' findings of impaired confrontation naming among 
dyslexics (Wolf & Obregon, 1 992; Swan & Goswami, 1 997). 

On Rapid Color and Object Naming, the present study did not find slowed rapid 
naming among the phonologically impaired groups compared to the controls (Tables 9 
and 24). As discussed previously, it was appropriate to compare just the impaired reader 
group (DPD) to dyslexic groups reported in the literature. Table 24 showed that although 
group means were in the direction consistent with that reported in the literature (Denckla 
& Rudel, 1976), the impaired reader group's score was not statistically different from the 
controls' score. It was possible that small sample sizes in the present study limited the 
power available to detect a difference between groups. This study only had sample sizes 
of 1 1 and 20 in the dyslexic and control groups respectively. This contrasted with sample 
sizes of 72 (dyslexic group) and 120 (control group) in Denckla and Rudel's (1976) study. 



* 106 

Effect sizes reflecting the difference between dyslexic (i.e., DPD) and normal readers in 

the present study were calculated. This yielded effect sizes of -.82 for Rapid Color 
Naming and -.67 for Rapid Object Naming, which are considered large. Unfortunately, 
Denckla and Rudel (1976) did not report means and standard deviations in their study; 
thus the present effect sizes cannot be compared to that of their study. Given the large 
effect sizes obtained, it is likely that with larger sample sizes, the present dyslexic 
subjects' rapid naming score would differ statistically from those of the controls'. 
However, despite the limited sample size, groups in this study were found to differ 
statistically on a confrontation naming task. This suggested that confrontation naming 
was more vulnerable to impairment than rapid naming in the present subject sample. 

Groups did not differ from each other on the experimental Naming Test, which 
used stimuli from the NAPM (Tables 9 and 24). Groups were not expected to differ on 
this test, as this test was designed to compose of objects highly familiar to the presem 
subjects' age range. The lack of a group difference indicated that groups were equally 
familiar with and knew the names of the objects used during the NAPM. 

Reaction Ti me and Response Accuracy 

PI vs. CTRL and DPD vs. ARPPvs CTRL Reaction time data showed that 
subjects responded to the naming task faster than to the visual match task, and that on the 
naming task, controls responded faster than phonologically impaired subjects. When the 
phonologically impaired group was divided into subgroups, the non-impaired reader 
group responded to the naming task faster than to the visual match task, similar to the 
controls. Perusal of Tables 1 1 and 26 did not suggest speed-accuracy trade-off The 



107 
phonologically impaired group was less accurate during Mouth Interference than during 

Foot Interference, and this was driven by their performance on the Visual Match Test. 

Together, data indicated that Visual Match was a more demanding task than our naming 

task, NAPM, and the Visual Match Test required longer response time. Mouth 

Interference was more demanding for phonologically impaired subjects than Foot 

Interference. Relevant group findings indicated that impaired readers differed from the 

non-impaired readers. While naming was easier for non-impaired readers and controls, it 

was more difficult for impaired readers. The naming task required as much processing 

time from the impaired readers as the Visual Match Test. 

Block effects were unexpectedly significant and were mainly driven by 

performance during the Visual Match Test. Subjects became both slower and less 

accurate on Visual Match as time went on. They became especially inaccurate during the 

Mouth Interference condition of the Visual Match. An obvious explanation was fatigue. 

However, the lack of a Block effect with the naming task argued against this explanation. 

Another more likely explanation was that Block effects were due to a stimulus artifact. 

The Visual Match stimuli were taken from the Test of Visual-Perceptual Skills (non- 

motor)-Revised (TVPS-R; Gardner, 1996), which had stimuli in increasing order of 

difficulty. In making the stimuli for the Visual Match Test, the approximate order of the 

stimuli appearing in the TVPS-R was kept. The naming task stimuli, in contrast, were 

designed to have approximately equal difficulty level. Word frequency, syllable length, 

and age of acquisition of names were variables that may affect difficulty level. Stimulus 

Sets A and B were balanced on these variables, and within each set, there was no 

ascending order of difficulty as represented via these variables. The Task X Block 



108 
interactions likely reflected an artifact of the Visual Match stimuli and were due to 

increasing order of difficulty of items. This artifact, however, could not explain why 
subjects became less accurate during the Mouth Interference condition in comparison to 
the Foot Interference condition of the Visual Match Test as time went on. The two 
stimulus sets of the Visual Match Test had equivalent difficulty level because the two sets 
were composed of the same trials with just different combination of items within each 
trial. Engaging in Mouth Interference during Block 2 resulted in less accurate responses 
on this task. The lack of a similar accuracy decrease with Foot Interference in Block 2 
suggested that Mouth Interference was harder or required more processing resources than 
Foot Interference. 

PAK vs. AAK . Grouping subjects by their AAT performance was done as dipost 
hoc procedure and was not expected to yield significam findings because only seven 
subjects fell into the PAK group. Findings were unexpectedly revealed. Subjects with 
poor articulatory knowledge were slower and less accurate than subjects with adequate 
articulatory knowledge in their ability to match phonemes. Response time on the 
Phoneme Match Test was entered as a covariate in analyses examining naming latency, 
which yielded an interesting interaction involving the covariate variable. During Block 1, 
subjects with slower response time to the Phoneme Match Test were disproportionately 
slower to name during Mouth Interi-erence (see Figure 2) in comparison to their response 
time during Foot Interference. Subjects with faster response time to the Phoneme Match 
Test did not demonstrate a difference in naming response time between Mouth and Foot 
Interference conditions. The slower subjects were to make judgments about phoneme 
match, the more they were interfered by the Mouth Interference condition. The faster 



109 
subjects were to make judgments about phoneme match, the less effect of interference 

condition (Mouth and Foot Interference) was seen. This interaction between Phoneme 
Match and Interference condition went away by Block 2, such that the difference between 
Mouth and Foot Interference response time remained similar regardless of subjects' 
abilit>' to match end phonemes (see Figure 3). 

The response time of subjects with poor articulatory knowledge improved during 
Block 2 while the response time of subjects with adequate articulator^' knowledge did 
not, and this was more salient during the Foot Interference than during the Mouth 
Interference condition (Table 35). The improvemem in the response time of subjects 
with poor articulatory knowledge may be a practice effect. Subjects with adequate 
articulatory knowledge did not show this practice effect because their reaction time was 
already fairiy comparable with that of the control group's (compare AAK group's RT on 
Table 35 with CTRL group's RT on Table 26). Subjects with adequate articulatory 
knowledge may be approaching floor effects on the naming task. That the response time 
of subjects with poor articulatory knowledge improved in the Foot Interference condition 
and not in the Mouth Interference condition suggested that Foot Interference was easier, 
less interfering, or made less demands from processing resources of these subjects. 

Together, these data showed that during Block 1. before practice effect 
manifested, subjects who were slow to match end phonemes (who were likely subjects 
with poor articulatory knowledge, as they were slower than subjects with adequate 
articulator knowledge on the Phoneme Match Test) were disproportionately slow to 
retneve names during Mouth Interference (Figure 2). This pattern went away by Block 2, 
concurtent with the improvement in naming latency of subjects with poor articulator 



110 
knowledge. Their improvement in naming latency was more salient during Foot 

Interference than during Mouth Interference (Table 35). For subjects who had poor 
articulatory knowledge, it was harder for them to retrieve names while engaging in 
mterfering mouth movements. Foot movements had a less interfering effect. With 
practice, subjects with poor articulator}' knowledge were able to improve their overall 
response time, thus diminishing their naming latency discrepancy between Mouth and 
Foot Interference conditions. 

The pattern described above was not present in the Visual Match data. Reaction 
time on this task revealed only a Block effect. Block 1 was responded to more quickly 
than Block 2, and accuracy data revealed that subjects were less accurate during Mouth 
Interference than Foot Interference in Block 2. These were the same findings discussed 
m the section comparing subjects grouped by reading achievement scores (PI vs. CTRL 
and DPD vs. ARPP vs. CTRL). The Block effect likely reflected a stimulus artifact. The 
Mouth Interference condition was more difficult than Foot Interference. 

Interference Movement Frequency 

PI vs. CTRL and DPD vs. ARPP vs CTRI . In addition to reaction time and 
response accuracy, the frequency of interference movements dunng each condition was 
also examined as a dependent variable. Analyses comparing phonological ly impaired 
with control subjects revealed a significant Group X Task X Interference X Block 
interaction. The phonologically impaired group produced interfering mouth movements 
at a slower frequency during name retrieval, and this was especially salient during Block 
1 (Table 18). The frequency by which these subjects produced interf-ering mouth 



Ill 

movements during the naming task was slower than dunng any other interference 
condition. Compared to the control group, the phonologically impaired subjects' 
mterfering movement index was slower only during the Mouth Interference condition of 
the naming task. When required to retrieve names, subjects with phonological 
impairment were less able to engage their articulators in another task. This was not due 
to the attentional demands of engaging in two tasks at once, because they were able to 
produce interfering foot movements with equal facility' as the control group. This also 
was not due to problems with oral praxis, because they were able to produce interfering 
mouth movements with equal facility as the controls dunng a nonverbal task. 

When the phonologically impaired group was divided into two subgroups, 
differences in the pattern of interfering movement frequency were shown. Both impaired 
readers and non-impaired readers were slower in producing interfenng mouth movements 
during naming, as consistent with the interaction reported above. However, during other 
conditions (i.e., naming Foot Interference, Visual Match interference conditions), there 
was a trend of faster interfering movement frequency by the non-impaired readers, even 
in comparison to controls, and a trend of slower interfenng movement frequency by 
impaired readers in comparison to controls (Table 28). Further indication of a difference 
between the two phonologically impaired groups was found in the correlation between 
interfering movement frequency index and the AAT score. There was no relationship 
between the control group's rate of interfering movement frequency and their AAT 
The impaired readers showed a positive con-elation beUveen their rate of interfering 
mouth movement and AAT score during the naming task only (Table 29). The better 
their obtained AAT score, the faster they were able to produce interfenng mouth 



score. 



112 
movements while engaging in a name retrieval task. This pattern was not shown by the 

non-impaired readers. Instead, they have a negative correlation between their AAT score 
and their rate of interfering foot movements during Visual Match. The better their 
articulatory awareness, the slower they were in producing interfering foot movements on 
a nonverbal, visual match test. 

The interference movement frequency data revealed differences between groups 
when response time and response accuracy data did not. This study had assumed that 
naming latency would be affected by the presence of a second interfering task. However, 
subjects focused their attention on the primary naming task and did not allow interfering 
movements to impinge upon their performance on this primary task. The interfering 
movements and name retrieval competed for neural resources. More resources turned out 
to have been directed to the primary naming task, resulting in a slowing down of the 
secondary, imerfering movement task. Rather than having mouth and foot movements 
interfere with the primary tasks as originally planned, the primary tasks turned out to 
have interfered with mouth and foot movements. This pattern was true for the 
phonologically impaired subjects only. They had to slow down the production of 
mterfering mouth movements because such movements interfered with their name 
retrieval. They could not simultaneously engage in both name retrieval and a motor task 
involving the articulators. This was important because it suggested that those who are 
phonologically impaired may be more dependent on the motor system than nonnal 
readers. 

PAK vs. AAK . Grouping subjects by their AAT performance yielded movement 
frequency findings similar to that reported for Phonologically Impaired vs. Controls. 



113 
Subjects with poor articuiatory knowledge produced interfering mouth movements more 

slowly than interfering foot movements while engaging in the naming task, and this 
pattern was not demonstrated by subjects with adequate articuiatory knowledge. The 
Group X Task X Interference interaction reflecting this pattern approached significance. 
Although this was only a trend, that the three-way interaction approached significance 
given the small sample size in the Poor Articuiatory Knowledge group was remarkable. 
The near presence of a high-level interaction with a group size of seven suggested 
robustness of this interaction. 

In addition to the primary aim of understanding the relationship between 
articuiatory awareness and name retrieval, Uvo other questions were addressed. The first 
of these secondary aims related to the influence of children with ADHD on the present 
data set. The second explored the relationship between articuiatory knowledge and 
phonological awareness, and variables that predicted them. 

Attention-D eficit/Hvp er activitv Disorder 

The high co-morbidity rate between reading disability and ADHD rendered 
attempts to limit ADHD in the present study impractical; what was learned about reading 
disabled children's neurological system would have limited general izability to the overall 
reading disabled population if ADHD^were considered an exclusionary criterion. Thus 
rather than excluding subjects with ADHD fi-om this study, these subjects were included 
and ADHD status was noted to track their influence on the overall data set. 

Subjects with ADHD tended to be slower and less accurate than their non-ADHD 
counterparts. Excluding subjects with ADHD did not change the overall pattern of the 



114 
data, suggesting that although ADHD subjects tended to be slower and less accurate, they 

responded to Interference conditions of NAPM and Visual Match Tests in a similar 
pattern as non-ADHD subjects. 

Relationship Between Articula torv Knowledge and Phonological Awarene<;s 

Predictors of articulatory knowledge were examined to explore if phonological 
awareness was related to it. Variables that correlated with AAT score included the LAC 
raw score (Tables 20) and the Phoneme Match accuracy. Examining groups individually 
revealed that the correlation between AAT and LAC was from the control group. The 
phonologically impaired readers' articulatory knowledge was not correlated with their 
LAC score. The validity of the LAC as a measure of phonological awareness was 
checked by its statistically significant correlation with performance on the Phoneme 
Match Test, a task that also required phonological processing. Although articulatory 
knowledge and phonological awareness was correlated for normal readers, that some 
subjects performed poorly on the LAC (i.e., phonologically impaired subjects) but 
adequately on the AAT (i.e., big variance on AAT score by phonologically impaired 
subject) indicated that articulatory knowledge and phonological awareness were 
dissociable phenomena. However, it should be noted that neither the AAT nor the LAC 
was psychometrically strong measures of articulatory knowledge and phonological 
awareness, respectively. Variance on both of these measures was large for all subject 
groups. These instruments represented available although not ideal measures of 
articulatory knowledge and phonological awareness. 



115 
The only variable that predicted articulatory knowledge level was the non-reading 

impaired group's Passage Comprehension performance. Phonological awareness was 

more correlated with other measures. With all of the subjects together, phonological 

awareness was positively correlated with age, BNT, and the three reading achievement 

subtests in addition to articulatory knowledge. The pattern of relationship between 

phonological awareness and each of these variables differed substantially by group. For 

the control group. Word Attack was most highly correlated with phonological awareness, 

not surprisingly as both Word Attack and LAC tested for phonological skills. Age was 

also correlated with phonological awareness, indicating that for nomial readers, 

phonological awareness developed with age. For subjects who were phonoiogically 

impaired, not surprisingly, there was no correlation between age and phonological 

awareness because by definition, these subjects were those for whom phonological 

awareness had failed to develop with age. Among the phonoiogically impaired, those 

who had better phonological skills also had higher confrontation naming and reading 

achievement scores. This probably reflected that there were high fiinctioning or well 

compensated readers who were phonoiogically impaired. Dividing the phonoiogically 

impaired group into impaired and non-impaired reader subgroups decreased the number 

of subjects available for these correlations to be significant. However, one negative 

relationship was maintained despite small group size. For non-impaired readers, better 

phonological awareness was correlated with slower rapid naming of color. The 

relationship between rapid object naming and phonological awareness was in the same 

direction as well. This negative relationship between rapid naming and phonological 



116 
skill may indicate that non-impaired readers slow down in speed in order to compensate 

for their phonological impairment. 

Unlike normal readers, those with phonological impairment did not develop 
phonological skills concurrently with articulatory knowledge. Among the non-impaired 
readers, phonological skills correlated negatively with rapid naming. The better the non- 
impaired readers' phonological skills, the more slowly they were able to rapidly name. 
Non-impaired readers as a group had worse phonological skills than the normal reader 
group. Although this was a phonologically impaired group, defined by poor Word Attack 
performance, data suggest that among these individuals, those with better phonological 
skills took longer to finish rapid naming tasks. The Rapid Color and Object Naming 
Tests used in this study scored the total time taken to complete the task and did not score 
separately for errors. Most subjects self-correct their errors, thus faulty performance was 
usually reflected in the longer time required to complete the task with self-correction. 
Perhaps non-impaired readers with better phonological skills were those who caught their 
mistakes and self-corrected, whereas those with very poor phonological skills were more 
likely to complete the task sloppily with little attention to the precision of their responses. 
Non-impaired readers' articulatory knowledge correlated positively with reading 
achievement. The better their articulatory knowledge, the better they did on reading 
achievement measures. It may be that these subjects use articulatory feedback as a 
compensatory mechanism to supplement their impaired phonological skills. Although 
these subjects have poor Word Attack score, their single-word reading and 
comprehension were commensurate with their expected achievement levels. They may 
be using the lexical route for reading with assistance from articulatoiy feedback. If so. 



117 
one would expect that these subjects would also use articulatory feedback in other 

situations where such feedback could facilitate language functioning, such as in naming. 
However, this was not supported by the lack of a correlation between AAT and BNT, 
AAT and rapid naming (Table 30), and AAT and NAPM reaction time and interference 
movement frequency- (Tables 23 and 29). 

The impaired readers did not evidence the same pattern as the non-impaired 
readers. Their poor phonological skills were not correlated with any variable, including 
articulatory knowledge. Unlike the non-impaired readers, the level of their phonological 
skill did not affect their speed of rapid naming, and their articulatory knowledge was not 
correlated with their reading achievement. This group, by definition, had poor single- 
word reading and comprehension in addition to poor phonological skills. In contrast with 
the non-impaired readers, who may be using the lexical route for reading, impaired 
readers appeared not to use the lexical or phonological routes for reading. Their LAC 
score. Word Attack, Word Identification, and Passage Comprehension scores were all 
worse than the non-impaired readers. Their AAT score was not statistically different 
fi-om that of the non-impaired readers although the direction of score differences between 
groups suggested that impaired readers may have the worst level of articulatory 
knowledge. The AAT's inability to differentiate groups on level of articulatory 
knowledge was a significant limitation of this study. 

A possible explanation for the differences just described was that the impaired 
readers represented the negative extreme on a continuum of phonological and reading 
skills. Their articulatory knowledge was not correlated with better reading achievement 
because basic skills were so poorly developed that compensatory strategies were not 



118 
sufficient to facilitate language functioning. Argument for a severity-of-impairment 

difference between impaired and non-impaired readers came from the data on the name 
retrieval task. Although impaired readers with better articulatory knowledge produced 
interfering mouth movements more quickly (positive correlation between AAT and 
interfering mouth movement frequency for the DPD group; Table 29), these same 
subjects were slower in their response time on the name retrieval task (negative 
correlation between AAT and NAPM Mouth Interference response time; Table 23). Thus 
the positive correlation between AAT and interfering mouth movement frequency did not 
necessarily mean that articulatory feedback did not influence naming; naming latency 
was negatively impacted and was slower. In fact, similar to the non-impaired readers, 
impaired readers' overall rate of interfering mouth movements had to slow down in order 
to perform the name retrieval task (Table 28). This was not due to dispersing attention 
between dual tasks because the same subjects were able to produce interfering foot 
movements at the same rate as control subjects while engaging in name retrieval. This 
also was not due to interfering mouth movements being harder to produce than 
interfering foot movements because all subjects produced both with equal facility on the 
nonverbal control task. The specificity of slowed interfering mouth movement during the 
name retrieval task suggested that articulatory feedback interacted with name retrieval for 
these subjects. 

Articulatory Feedback Hvpothesis of Naming 

The most direct test of the articulatory feedback hypothesis of naming was 
through examining performance patterns of those with poor articulatory knowledge 



119 



(PAK) and those with adequate amculatory knowledge (AAK). Grouping subjects by 
reading achievement turned out to be an indirect way to test this hypothesis, as 
phonologically impaired readers turned out not to have worse articulator^' knowledge 
compared to normal readers. 

The articulatory feedback hypothesis of naming posited that for individuals with 
adequate articulatory knowledge, articulatory feedback facilitates name retrieval. 
Interfering with this feedback should slow down name retrieval. Individuals with poor 
articulatory knowledge should respond differently. Although naming latency data did not 
reveal theoreticall>- important findings, interfenng movemem frequency data showed that 
subjects with poor articulator^' knowledge tended to produce interfering mouth 
movements most slowly while engaging in name retrieval. Subjects with adequate 
articulatory knowledge produced such movements at the same rate as during other control 
conditions. In other words, for subjects with adequate articulatory knowledge, 
articulatory feedback (i.e.. Foot Interference) and no articulatory feedback (i.e.. Mouth 
Interference) conditions did not make a difference in their response latency or frequency 
of interfering movements produced. They did not spontaneously use articulatory 
feedback to assist with name retrieval when feedback was available (i.e.. Foot 
Interference latency). Providing inappropriate articulatory feedback did not slow down 
their name retrieval (i.e.. Mouth Interference latency). Engaging in a naming task did not 
differentially influence the facility of their ability to produce interfenng mouth or foot 
movements. 

Individuals xvith poor articulatory knowledge responded differently. Engaging in 
name retrieval interfered with their mouth movements and slowed down the frequency of 



120 



mouth movements. Naming and interfering mouth movements competed for the same 
neural resource. When naming was attended to as a primary task, interfering mouth 
movements must slow down. However naming did not compete with foot movements. 
Subjects with poor articulatory knowledge were able to name and produce interfering 
foot movements with the same facility as the control subjects. Something about mouth 
movements interfered with name retneval, suggesting that these two processes share 
some neural connectivity. The decreased rate of mouth movement production must be 
related to this shared connectivity and can not due to this being a "harder" interference 
task because all subjects were able to produce mouth and foot movements with equal 
facility during a nonverbal control task. 

In the present study, subjects attended to the naming task as the primary task and 
sacrificed performance on the secondary interference task. If they had been instructed to 
maintain a constant mouth movement, making this the primary task, the shared 
connectivit>' beUveen mouth movement and name retrieval would likely cause these 
subjects' response time on naming tasks to slow down in comparison to their response 
latency while maintaining a constant foot movement. This indirectly supported that 
inappropnate articulatory feedback inhibited name retrieval. However, this inhibition 
effect was seen in those with poor articulatory knowledge rather than in those with 
adequate articulator>' knowledge. For individuals who have difficulty knowing the 
position of their articulators while making speech sounds, articulatory feedback affected 
their naming process. Individuals who have a good sense of articulatory position were 
not affected by articulatory feedback during name retrieval. This difference between 
groups may be due to efficiency of language processing. For those who have well- 



> *■?<■'? 



121 
developed language systems, name retrieval was executed automatically and did not 

require much neural resources. Inappropriate articulatory feedback did not slow down 

naming because sufficient resources were available to process both the feedback and 

name retrieval at the same time. Compromised articulatory awareness may reflect 

abnormal development of a language-related neural system. Under compromised 

conditions, name retrieval, a language task, could not be executed as automatically as 

under uncompromised conditions. More neural resources were necessary to complete the 

same task. Inappropriate articulators feedback competed for the same neural resource as 

that needed for naming, resulting in inefficient processing of one of these tasks. 

Limitations 

The interference paradigm used in this study allowed for the examination of 
naming latency in the presence of excess, irrelevant information making demands for 
neural resources. This allowed for the examination of inhibition effects, not facilitation 
effects. Therefore the modification of the hypothesis can address factors that impede 
naming, but whether appropriate articulatory feedback facilitates naming was not directly 
addressed by the design of this study and remains an empirical question. 

The assessment of articulatory knowledge in the present study was based on the 
Articulatory Awareness Test. This was an experimental instrument that has not 
undergone ngorous validity and reliability testing to prove its efficacy in accurately 
identifymg those with good articulatory knowledge from those without articulatory 
knowledge. This instrument was used because it was the only one known to assess the 
level of articulatory knowledge. The present data showed that both normal and 



122 
phonologically impaired readers vary greatly on their performance on this task. That is, 

readmg achievement and articulatory knowledge dissociated and were not dependent on 
each other. While this may be a "true" finding, replication is necessary to rule out this as 
an artifact of the assessing instrument. 

There were several limitations related to methodology. First, interference 
movements were not successfiil in interfering with the naming task. Subject attended to 
the naming task as the primary task instead, and this primary task affected the rate of 
production of the mouth and foot movements. The lack of an interference effect on 
naming latency precluded direct conclusions about whether inappropriate feedback 
inhibited naming latency. This must be mferred mdirectly. A better methodology would 
have been to control for the rate of interfering movements to allow for examination of 
inhibition effects on naming latency. 

Second, unexpected Block effects were found. This was likely due to a stimulus 
artifact in the Visual Match stimulus sets. These stimuli were unintentionally arranged in 
order of increasmg difficulty, with more difficult items appearing the in the second half 
of the trials. Because the block effects found mamly involved the Visual Match Test, 
other factors, such as fatigue or practice effects, were not likely to have been operative. 
However, these confounding factors cannot be completely ruled out. 

Finally, although the two NAPM stimulus sets were balanced on word frequency, 
number of syllables, and grade level by which the word is taught, they still turned out to 
differ in difficulty level. Subjects responded to Set A faster than to Set B. Stimulus sets 
were counterbalanced across interfering conditions and across subjects. This randomly 
dispersed the vanance introduced by differem difficulty levels of the two stimulus sets in 



123 
the study. It is not known whether more balanced stimulus sets would have reduced 

random variance sufficiently for naming latency difference between conditions/groups to 

reach significance. 

Summar\' of Findings 

With these limitations in mind, findings of this study could be summarized as 
follows: 

Correlation Between Articulatory Knowledge and Naming 

For normal readers, articulatory knowledge and name retrieval were related. 
Although normal readers did not spontaneously use articulatory feedback to assist 
naming, when their attention was drawn to their articulators, better articulatory 
knowledge was associated with faster naming latency. This association was not due to 
articulatory feedback. It may be related to "spreading" activation to the name retrieval 
systems from pre-motor and motor areas controlling the articulators. 

Dvslexics Do Not Have Worse Articulatory Knowledge 

t 

Montgomery's ( 198 1 ) finding that dyslexics have impaired articulatory 
knowledge was not wholly supported. Both normal and phonologically impaired readers 
demonstrated a wide range of articulatory knowledge. The present version of the 
Articulatory Awareness Test may not have the psychometric properties required to 
differentiate groups by their level of articulatory knowledge even if a "true" difference 
existed. Articulatory knowledge and reading achievement were correlated only for 



124 
phonologically impaired readers who have adequate single-word reading and 

comprehension skills. The possibility that these individuals use articulatory knowledge 

to help them compensate and thus achieve expected reading levels was posited. 

Relationship Between Articulatory- Knowledge and Phonological Awareness 

Articulatory knowledge and phonological awareness were correlated among 
normal readers but not for phonologically impaired readers. Because intact articulatory 
knowledge can manifest in a person with impaired phonological awareness, these are 
dissociable phenomena. 

Modification of the Articulatorv Feedback Hvpothesis of Naming 

Inappropriate articulatory feedback impeded naming in those who have poor 
articulatory knowledge. Those with compromised articulatory knowledge were more 
vulnerable to be disrupted in the name retrieval process by irrelevant linguistic 
information. Individuals with adequate articulatory knowledge were free from such 
vulnerability, probably because their efficiency of processing freed neural resources to 
handle extraneous information. Among normal readers, heightened articulatory 
knowledge was associated with faster naming latency. However, articulatory feedback 
per se was not the major contributor to this relationship, because this positive correlation 
beUveen articulatory knowledge and faster naming latency occurred during the presence 
of articulatory feedback that was inappropriate to the naming task at hand. This study 
found no data to support that articulatory feedback was used spontaneously to facilitate 
naming. 



APPENDIX 1 
ARTICULATORY AWARENESS TEST 



Name 



Date of Birth 

Grade 

Race 



Date 

Age 

Sex 



Handedness 



Pantomime 



The Exammer should put out picture #8 (/a/) and go over the areas of the mouth 
making sure that the patient is aware of the tongue, lips, and teeth. After the Examiner 
has reviewed these, the patient should be asked to imitate the position that is seen with 



Efficiency Rating 



vuiua v>, /, anu \j. 


rn- 


Picture # 


Pantomime Score 




- (+/-) 


/«/ 


1. 


ni 


2. 


161 


3. 


Practice Items 





(1 -groping, 2-slow, 3-easy) 



(The cards are numbered on the back. The Examiner holds them in a fan (as with 
a card game) with the numbers facing the Examiner to allow easy access to test items). 



125 



126 



Examiner: "Now I am going to say a sound, and I want you to repeat the sound 
after me several times. As you repeat the sound I want you to think about how you are 
making the sound." 

Produce /a/ "as in apple" and have the patient say it after you several times. Do 
not let the patient see your mouth as you say it [hold cards in fi-ont of Examiner's mouth]. 

"Now 1 want you to feel the sound as you say it and then point to the picture 
which matches how your mouth feels when you make the sound." 

Place cards, 8, 3, 6 in fi-ont of the patient to be chosen fi-om. Praise correct 
response and ask why he/she chose that picture. If incorrect, review the three pictures as 
before (i.e., placement of tongue, lips, and teeth). Then have the patient produce the 
sound again and select the corresponding picture. 

Follow the same procedure for /p/ (cards 1, 7, 2) and /th/ (5, 2, 1). 

Practice Item Score Efficiencv Rating 

(+/-) (1,2,3) 

1. a apple (H 3 6) 

2. D pie (1 7 2) 

3. th that (5 2 1) 



. ' 127 

Test Items 

Directions 

"Let's try a few more. Listen to the sound I say and repeat it after me several 
times. Remember, as you say the sound, think about how the sound feels as you are 
making the sound. Then choose the picture which shows you how you are making the 
sound. I'll show you one card at a time. When you see the one that matches, tell me, and 
we will go on to the next sound. Only one card will match, but sometimes I'll be tricky 
and no card will match. Ready*^" ^ . . .,1 _^ 

[If the patient produces a sound in an unusual way (e.g., /I/ with tongue between 
teeth), none of the pictures may be representative, so a "none" response is "+".] 



ICSI 


uciii;) 




bcore 

(+/-) 


Oualitv 

(1,2,3) 


1. 


s 


. (17 5) 








saw 




2. 


V 


. (3 6 4) 







vase 
3. t (8 3 2) 

table 

4- th (2 7 5) 
thermometer 

5- b (7 1 8) 
bicycle 



128 



6. 1 (6 4 2) 
ladder 

7. d (5 8 1) 
dog 

8- __iL_^ (6 1 4) 
- fish 

9. e (8 2 7) 
exit 

10. k (3 5 1) 
kite 



Total 



Did the subject use hands/fingers on mouth? Yes No 

Did the subject try a mirror? Yes No 



Please note if subject produces sound differently, e.g., tongue out or flat for 



/I/. 



,,i^iMV^i\.l 



129 



Experimental Test Items 



Test Items 


11. 


m 




mom 


12. 


ee 




each 


13. 


z 




zap 


14. 


e 




gas 


15. 


a 




ant 


16. 


n 




nice 


17. 


th 




thick 


18. 


1 




leg 


19. 


D 



Score Quality 

(+/-) (1,2,3) 



(5 6 7) 



(7 2 8) 



(3 7 5) 



(6 1 3) 



(5 4 8) 



(7 6 3) 



(1 2 8) 



(1 6 5) 



puppy 



20. d (3 2 1) 

dance 



Experimental Total 
Total from #1-10 

Combined Total 



130 



: -^■^' i;'yj ^/,\:^'i 



♦ . 



\J 



Card 1 : /k/, /g/ r 



131 




Card 3: /t/, /d/, /n/ 



132 




V. A 



Card 4: /I/ 




133 



Card 5: /s/, Izl 




Card6;/f/,/v/ 







/%jL 









W ;*.' * ^" 



134 



Card7:/p/,/b/,/m/ 




Card 8: /a/. Id 




APPENDIX 2 
ATTENTION-DEFICIT/HYPERACTIVITY DISORDER INTERXTEW 



^"bj^'^t: __^ ^^^^. 



ADHD Questionnaire 
Inattention 

a. Does your child often fail to give close attention to details or make careless 
mistakes in schoolwork or other activities? 

b. Does s/he often have difficulty sustaining attention in tasks or play? 

c. Does s/he often not seem to listen when spoken to directly? 

d. Does s/he often not follow through on instructions and fail to finish schoolwork 
chores (not due to oppositional behavior or failure to understand instruction)? 

e. Does s/he often have difficulty organizing tasks and activities? 



or 



135 



136 
f. Does s/he often avoid, dislike, or is reluctant to engage in tasks that require 

sustained mental effort (such as schoolvvork or homework)? 



g. Does s,/he often lose things necessary for tasks or activities (e.g., tovs, school 
assignment, pencils, or books)? 

h. Is s/he often easily distracted by extraneous stimuli? 

i. Is s/he often forgetful in daily activities? 

Hyperactivity 

a. Does s/he often fidget with hands or feet or squirm in his/her seat? 

b. Does s/he often leave his/her seat in classroom or in other situation in which 
remaining seated is expected? 

c. Does s/he often run about or climbs excessively in situations in which it is 
inappropriate? 

d. Does s/he often have difficult)' playing or engagmg m leisure activities quietly? 

e. Is s/he often "on the go" or often act as if "driven by a motor?" 



137 



f. Does s/he often talk excessively? 



Impulsivity 



g. Does s/he often blurt out answers before questions have been completed? 



h. Does s/he have difficulty awaiting turn? 



'■ • v.. 



i. Does s/he often interrupt or intrude on others (e.g., butts into conversations or 

games)? 

When did you first notice these behaviors? 
How long did these behaviors last? 



Do you think your child's behaviors are worse than can be expected given his/her age? 



Where do you notice these behaviors? Are they present in more than one situation? 



How have these behaviors affected your child's social or academic functioning? 



138 



314.00 ADHD, Predominantly Inattentive 
Inattention (6) 

314.01 ADHD, Combined (6-6) or Predominantly Hyperactive-Impulsive (6) 
Hyperactivity Impulsivity 



6 mo 

Inconsistent \v/ developmental level 

before 7 yrs 

> 1 setting 

social or academic impairment 



'J ' >: • 



V - - V . f 









APPENDIX 3 
NAPM STIMULI 








Stimulus Set 






Stimulus Set 






A 








B 






Al 


Gun 


Sun 


Y 


Bl 


Finger 


Tiger 


Y 


A2 


Parrot 


Carrot 


Y 


B2 


Knife 


Nose 


N 


A3 


Violin 


Gorilla 


N 


B3 


Star 


Car 


Y 


A4 


Bird 


Flag 


N 


B4 


Balloon 


Scissors 


N 


A5 


Shovel 


Basket 


N 


B5 


Ball 


Frog 


N 


A6 


Hair 


Pear 


Y 


B6 


Magnet 


Pirate 


Y 


A7 


Dress 


Lamp 


N 


B7 


Vest 


Nest 


Y 


A8 


Wagon 


Dragon 


Y 


B8 


Barrel 


Sandwich 


N 


A9 


Volcano 


Piano 


Y 


B9 


Cake 


Snake 


Y 


AlO 


Broom 


Eye 


N 


BIO 


Mitten 


Kitten 


Y 


All 


Pumpkin 


Napkin 


Y 


Bll 


Squirrel 


Clothespin 


N 


A12 


Elephant 


Strawberry 


N 


B12 


Medal 


Needle 


Y 


A13 


Seal 


Wheel 


Y 


B13 


Bed 


Bread 


Y 


A14 


Swan 


Comb 


N 


B14 


Kite 


Book 


N 


A15 


Mushroom 


Wallet 


N 


B15 


Bam 


Pipe 


N 


A16 


Tree 


Key 


Y 


B16 


Canoe 


Bamboo 


Y 


A17 


Camel 


Banana 


N 


B17 


Guitar 


Monkey 


N 



139 



140 



A18 


Mountain 


Fountain 


Y 


B18 


Axe 


Bowl 


N 


A19 


Clovvn 


Crown 


Y 


B19 


Bee 


Knee 


Y 


A20 


Owl 


Bus 


N 


B20 


Ladder 


Camera 


N 


A21 


Dolphin 


Coffm 


Y 


B21 


Ear 


Deer 


Y 


A22 


Umbrella 


Porcupine 


N 


B22 


Saddle 


Feather 


N 


A23 


Leaf 


Drum 


N 


B23 


Muscle 


Pencil 


Y 


A24 


Cat 


Hat 


Y 


B24 


Sheep 


Purse 


N 


A25 


Flower 


Shower 


Y 


B25 


Bear 


Chair 


Y 


A26 


Bow 


Fish 


N 


B26 


Fox 


Box 


Y 


A27 


Necktie 


Butterfly 


Y 


B27 


Pocket 


Rocket 


Y 


A28 


Chimney 


Zebra 


N 


B28 


Pie 


Whale 


N 


A29 


Lock 


Clock 


Y 


B29 


Bucket 


Turkey 


N 


A30 


Dog 


Cup 


N 


B30 


Folder 


Shoulder 


Y 


A31 


Giraffe 


Hotdog 


N 


B31 


Sweater 


Rabbit 


N 


A32 


Moon 

v/XT ,• i; X 


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' fj ^ ^ U - » 



BIOGRAPHICAL SKETCH 



Lisa Hsiao-Jung Lu was bom in Taipei, Taiwan, in 197L She immigrated to the 
United States with her parents in 1980 and completed her secondary education in Tucson, 
Arizona. She obtained her Bachelor of Arts degree summa cum laude from the 
Washington University in St. Louis, where she majored in psychology and minored in 
English literature. During her study there, she developed an interest in neuropsychology 
and decided to pursue this formally at the University of Florida, Department of Clinical 
and Health Psychology. Over the course of her graduate study, she developed an interest 
in the neuropsychological layout of the language system within the central nervous 
system. Her initial work in this area involved understandmg the semantic organization of 
the language system through studying category-specific naming deficits. This research 
earned her recognition from the American Psychological Foundation, the Manfred Meier 
scholarship. Lisa H. Lu also has an interest in the developmental aspects of language. 
The present dissertation represents her initial query into the development of 
neuropsychological subcomponents that work together to yield a ftinctional language 
system. She hopes to further this line of inquiry by studying dysfunction within a 
pictographic reading and wnting system, the Chinese language. By studying two 
manifestations of language, English and Chinese, she hopes to contribute to 
neuropsychology's understanding of nervous system components that work together to 
allow the complex behavior we call language. 



146 



I certify that I have read this study and that in my opinion it conforms to 
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, 
as a dissertation for the degree of Doctor of Philosophy. 



Eileen B. Fennell, Chair 

Professor of Clinical and Health Psychology 



I certify that I have read this study and that in my opinion it conforms to 
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, 
as a dissertation for the degree of Doctor of Philosophy. 




^ruce Crosson 
Professor of Clinical and Health Psychology 

I certify that I have read this study and that in my opinion it conforms to 
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, 
as a dissertation for the degree of Doctor of Philosophy. 



D. 



TAdL 



Duane E. Dede 

Clinical Associate Professor of 
Clinical and Health Psychology 



I certify that I have read this study and that in my opinion it conforms to 
acceptable standards of scholarly presentation and is fully adequate, in scope and quality 
as a dissertation for the degree of Doctor of Philosophy. 




Kenneth M. Heilman 
Distinguished Professor of Clinical and 
Health Psychology 



I certify that I have read this study and that in my opinion it conforms to 
acceptable standards of scholarly presentation and is fully adequate, in scope and quality 
as a dissertation for the degree of Doctor of Philosophy 




/\./V^vfi>\. 



Jarnes J. Algina 
Professor of Edui 




This dissertation was submitted to the Graduate Faculty of the College of Health 
Professions and to the Graduate School and was accepted as partial fulfillment of the 
requirements for the degree of Doctor of Philosophy. 



August, 2000 



^^ri^ CrP^ 



Dean, College of Health Profession 



Dean, Graduate School 









Lisa Hsiao-Jung Lu 

5020 S. Lake Shore Dr., #121 1 

Chicago, IL 60615 

773-643-8875 

LhluigiyahcxLcom 

August 1, 2000 



College of Health Professions 

University of Florida ~ - 

Box 100185 

Gainesville, FL 31610-0185 

RE: Dissertation 

Dear College Office: 

Enclosed is the final version of my dissertation for your records. If you have any 
questions, please contact me via phone or email. Thank you very much! 

Sincerely, 

Lisa Hsiao-Jung Lu 



7Tt- T^^\ T^T 



UNIVERSITY OF FLORIDA 



3 1262 08555 2726